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1 These authors contributed equally to this article.
Inmaculada Moreno
Footnotes
1 These authors contributed equally to this article.
Affiliations
Department of Research and Development, Igenomix SL, Valencia, SpainDepartment of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA
1 These authors contributed equally to this article.
Felipe Vilella
Footnotes
1 These authors contributed equally to this article.
Affiliations
Fundación Instituto Valenciano de Infertilidad (IVI), Valencia, SpainInstituto de Investigación Sanitaria INCLIVA, Valencia, SpainDepartment of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA
IVI Valencia, Valencia, SpainDepartment of Pediatrics, Obstetrics, and Gynecology, Universidad de Valencia, Instituto Universitario IVI, Valencia, Spain
2 These authors contributed equally to this article.
Affiliations
Scientific director, Igenomix SL, Valencia, SpainFundación Instituto Valenciano de Infertilidad (IVI), Valencia, SpainInstituto de Investigación Sanitaria INCLIVA, Valencia, SpainDepartment of Pediatrics, Obstetrics, and Gynecology, Universidad de Valencia, Instituto Universitario IVI, Valencia, SpainDepartment of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA
Bacterial cells in the human body account for 1–3% of total body weight and are at least equal in number to human cells. Recent research has focused on understanding how the different bacterial communities in the body (eg, gut, respiratory, skin, and vaginal microbiomes) predispose to health and disease. The microbiota of the reproductive tract has been inferred from the vaginal bacterial communities, and the uterus has been classically considered a sterile cavity. However, while the vaginal microbiota has been investigated in depth, there is a paucity of consistent data regarding the existence of an endometrial microbiota and its possible impact in reproductive function.
Objective
This study sought to test the existence of an endometrial microbiota that differs from that in the vagina, assess its hormonal regulation, and analyze the impact of the endometrial microbial community on reproductive outcome in infertile patients undergoing in vitro fertilization.
Study Design
To identify the existence of an endometrial microbiota, paired samples of endometrial fluid and vaginal aspirates were obtained simultaneously from 13 fertile women in prereceptive and receptive phases within the same menstrual cycle (total samples analyzed n = 52). To investigate the hormonal regulation of the endometrial microbiota during the acquisition of endometrial receptivity, endometrial fluid was collected at prereceptive and receptive phases within the same cycle from 22 fertile women (n = 44). Finally, the reproductive impact of an altered endometrial microbiota in endometrial fluid was assessed by implantation, ongoing pregnancy, and live birth rates in 35 infertile patients undergoing in vitro fertilization (total samples n = 41) with a receptive endometrium diagnosed using the endometrial receptivity array. Genomic DNA was obtained either from endometrial fluid or vaginal aspirate and sequenced by 454 pyrosequencing of the V3–V5 region of the 16S ribosomal RNA (rRNA) gene; the resulting sequences were taxonomically assigned using QIIME. Data analysis was performed using R packages. The χ2 test, Student t test, and analysis of variance were used for statistical analyses.
Results
When bacterial communities from paired endometrial fluid and vaginal aspirate samples within the same subjects were interrogated, different bacterial communities were detected between the uterine cavity and the vagina of some subjects. Based on its composition, the microbiota in the endometrial fluid, comprising up to 191 operational taxonomic units, was defined as a Lactobacillus-dominated microbiota (>90% Lactobacillus spp.) or a non-Lactobacillus-dominated microbiota (<90% Lactobacillus spp. with >10% of other bacteria). Although the endometrial microbiota was not hormonally regulated during the acquisition of endometrial receptivity, the presence of a non-Lactobacillus-dominated microbiota in a receptive endometrium was associated with significant decreases in implantation [60.7% vs 23.1% (P = .02)], pregnancy [70.6% vs 33.3% (P = .03)], ongoing pregnancy [58.8% vs 13.3% (P = .02)], and live birth [58.8% vs 6.7% (P = .002)] rates.
Conclusion
Our results demonstrate the existence of an endometrial microbiota that is highly stable during the acquisition of endometrial receptivity. However, pathological modification of its profile is associated with poor reproductive outcomes for in vitro fertilization patients. This finding adds a novel microbiological dimension to the reproductive process.
and most of them are not easily culturable. The vaginal microbiota was first identified in 2002 by molecular methods used to detect nonculturable bacteria.
Evaluation of the bacterial vaginal flora of 20 postmenopausal women by direct (Nugent score) and molecular (polymerase chain reaction and denaturing gradient gel electrophoresis) techniques.
A normal vaginal microbiota is defined by the presence of bacterial species (spp.) of the Lactobacillus genus that are commonly associated with a healthy genitourinary status. The vaginal microbiota typically changes throughout the menstrual cycle, depending on factors such as vaginal hygiene, sexual activity, use of intimate products, and underwear composition; greater microbiota stability is associated with the estradiol peak at ovulation and progesterone rise in the midluteal phase.
However, alterations in the vaginal microbiota can lead to several pathologies. For example, bacterial vaginosis (BV) is a vaginal syndrome produced by the overgrowth of anaerobic bacteria such as Atopobium vaginae, Gardnerella vaginalis, Mobiluncus curtisii, and Mycoplasma hominis to the detriment of Lactobacillus spp.
Evaluation of the bacterial vaginal flora of 20 postmenopausal women by direct (Nugent score) and molecular (polymerase chain reaction and denaturing gradient gel electrophoresis) techniques.
Interestingly, a microbiological culture of the tip of the transfer catheter in patients undergoing in vitro fertilization (IVF) revealed that the presence of bacterial species in the uterine cavity at the time of embryo transfer negatively affects implantation and pregnancy rates. Indeed, Enterobacteriaceae spp., Streptococcus spp., Staphylococcus spp., Escherichia coli, and Gram-negative bacteria have been associated with decreased implantation rates and poor pregnancy outcomes,
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.
but no consensus has been reached regarding the origin and genus of bacterial pathogens and the mechanisms by which they could interfere with embryonic implantation. Importantly, prior studies were limited by the number of bacterial species that can be isolated and identified following culture of the catheter tip, and the potential risk of contamination of the catheter tip in the vagina/ectocervix/endocervix.
Although the vagina has long been known to contain microbes, the uterine cavity was classically considered a sterile organ.
A report challenging this dogma suggested the existence of an endometrial microbiota comprising different microorganisms (Lactobacillus spp., Mycoplasma hominis, Gardnerella vaginalis, and Enterobacter spp.) isolated by classic microbiological culture techniques of endometrial samples obtained from hysterectomy.
Recently, the molecular identification of bacterial species in the endometrium of asymptomatic patients undergoing hysterectomy for benign indications confirmed that the uterine cavity is not sterile.
Although a pathological infection is not always produced by the host-microbiota interactions, a murine model of ascending bacterial infection supports the concept that the endometrium might not be as sterile as thought.
the potential for an endometrial microbiota that contributes to reproductive health merits investigation.
Here, we investigated the existence of a differentiated endometrial microbiota using 16S rRNA gene pyrosequencing, and assessed its hormonal regulation and potential functional impact on reproductive outcome in patients undergoing IVF. The findings imply a role for the endometrial microbiota in reproductive outcomes.
Materials and Methods
Study design
Three separate prospective pilot studies were performed. First, to analyze the existence of a differential endometrial microbiota, paired samples of endometrial fluid (EF) and vaginal aspirates (VA) were obtained simultaneously from 13 fertile women in their prereceptive (two days after the luteinizing hormone surge, known as LH+2) and receptive (seven days after the uteinizing hormone surge, also known as LH+7) phases in the same menstrual natural cycle (total samples analyzed n = 52). To investigate the hormonal regulation of the endometrial microbiota, EF samples (n = 44) were collected at LH+2 and LH+7 within the same natural cycle from 22 fertile women. Finally, the reproductive impact of an altered endometrial microbiota in EF was assessed by implantation, miscarriage, ongoing pregnancy, and live birth rates in infertile subjects undergoing IVF (n = 35) in whom a receptive endometrium was diagnosed using the endometrial receptivity array (ERA). EF samples were obtained in the cycle before embryo transfer. Approval for this study was obtained from the IVI Valencia Ethical Committee (project: 1404-FIVI-015-CS), and subjects provided written informed consent.
Subjects
For comparison between vaginal and endometrial bacterial communities and the study of hormonal regulation of the endometrial microbiota, samples were obtained within the natural cycles of women from the ovum donation program at IVI Valencia, Spain. The functional impact of the endometrial microbiota on reproductive outcome was explored in infertile subjects undergoing IVF treatment in whom a receptive endometrium was diagnosed by ERA (Igenomix SL, Valencia, Spain). A more detailed description is provided in Supplementary Methods.
EF aspiration
EF was obtained in all subjects as previously described.
Isolation of DNA from frozen EF or VA was performed following the MagNa Pure compact nucleic acid isolation kit I (Roche, Madison, WI) protocol with modifications. A more detailed protocol is provided in Supplementary Methods.
Polymerase chain reaction and 16S rRNA sequencing
For sequencing and barcoding, the V3-V5 region of the 16S rRNA gene was amplified using key-tagged eubacterial primers, as previously described.
Unidirectional pyrosequencing was carried out on a 454 Life Sciences GS FLX+ instrument (Roche) following the Roche Amplicon Lib-L according to manufacturer's protocol. A more detailed protocol for polymerase chain reaction is described in Supplementary Methods.
Taxonomic assignment and bioinformatics
Taxonomic classifications
Sequences were treated for quality control prior to taxonomic classification (Supplementary Methods). QIIME
was utilized to produce operational taxonomic units (OTU) clusters and classifications. All the processed 16S rRNA sequences were clustered into OTUs based on their sequence similarity using UCLUST
algorithm, setting the sequence similarity threshold to 0.97. For each OTU, a representative sequence was selected for downstream analysis. Taxonomy was assigned to each representative sequence using the ribosomal database project classifier method v2.2.
The taxonomic assignments that resulted in no more than 2 sequence reads assigned to a genus, and with a mean ribosomal database project score of <0.9, were considered to be low-quality and were excluded from the community structure analysis. This filtering eliminated a total of 94 low-quality genus assignments for pilot 1, 164 for pilot 2, and 147 for pilot 3. All sequence data were deposited in http://www.ncbi.nlm.nih.gov/sra/ under the SRP078557 accession.
Alpha diversity
QIIME was used to calculate alpha diversity and rarefaction curves before filtering. Shannon
methods were employed to analyze the biodiversity within a group of samples. Plots were generated by QIIME, with the number of sequences on the x-axis and the corresponding alpha diversity index on the y-axis. In rarefaction curves, if the lines for some categories do not extend, that means that at least one of the samples in that category does not reach that number of sequences. The shape on the horizontal axis plot serves as an indicator of richness: greater y-axis values indicate more species richness and lower values indicate the opposite.
Community clustering analysis
The clustering of communities was done using Bray-Curtis distance and hierarchical clustering with R vegan package.
Principal component analysis of microbial communities
Principal component analysis (PCA) was generated using the prcomp routine in the R package on a data set consisting of the percentage abundances of taxa in each community. The 2 principal components explained 88% of the variance. Principal coordinates analysis (PCoA) plots were generated using Bray-Curtis distances.
Statistical analysis
Statistical analysis on bacterial taxonomic identification was performed using R version 3.1.1. Supervised machine learning models were performed in R (http://www.R-project.org) using different packages from CRAN, ROCR for receiver operating characteristic curves, rpart for classification and regression tree (CART), and generalized linear model for logistic regression. A comparison of quantitative variables was done using Student t test or analysis of variance test for independent samples (depending on whether ≥2 groups were compared). The Adonis function in the R package vegan was used to conduct nonparametric multivariate analysis of variance. For comparing categorical data, χ2 test was performed. P < .05 was considered statistically significant.
Results
Endometrial and vaginal microbiota differ in some asymptomatic subjects
To assess any potential experimental bias due to the primer set used in our study, the microbial communities identified in VA using the V3–V5 hypervariable regions of the 16S rRNA gene (Figure 1, right column) were compared to the bacterial taxa previously reported in vaginal samples by other groups using the V1-V2 hypervariable regions.
The results demonstrate the identification of the same bacterial OTUs in vaginal samples independent of the set of primers used for sequencing (OTU list is detailed in Table S1). Then, to test the existence of a differential endometrial microbiota, paired samples of EF (n = 26) and VA (n = 26) from 13 fertile subjects were obtained and their bacterial communities investigated by pyrosequencing of the variable regions V3-V5 of the bacterial 16S rRNA gene. Different OTUs were identified between endometrial and vaginal samples (Figures 1, 2, and 3, A , and Table S1). From them, 9 samples were colonized only by Lactobacillus spp., while the rest of the samples were colonized by combinations of different OTUs in addition to Lactobacillus. Only 2 of 26 pairs of samples showed the presence of the same bacterial OTUs in endometrium and vagina (Table S1). In the remaining 24 pairs of samples, small differences between endometrial and vaginal microbiota were found with OTUs present in endometrium but not in the vagina, and vice versa, although some of those differences were nearly imperceptible and only contributed small percentages (Figure 4). Interestingly, in 6 paired samples the bacterial communities in the endometrium and vagina were completely different, with a high proportion of potential pathogens belonging to Atopobium, Clostridium, Gardnerella, Megasphaera, Parvimonas, Prevotella, Sphingomonas, or Sneathia genera found in the endometrium but not in the vagina (n = 5: subject 1 at LH+2, subject 4 at LH+2, subject 9 at LH+7, subject 10 at LH+7, and subject 13 at LH+2), or potential bacterial pathogens (Gardnerella, Clostridium, Sneathia, or Prevotella spp.) in the vagina that were not present in the endometrium (n = 1: subject 11 at LH+2) (Figure 2). Combinations of up to 54 bacterial OTUs formed the microbial endometrial communities, while in vagina we detected up to 20 genera. Lactobacillus was found as the major genus in all EF and VA analyzed, but in different percentages between women. Bacterial genera such as Atopobium, Gardnerella, Prevotella, or Sneathia were also commonly identified in both endometrial and vaginal samples. With the present design we demonstrated that the endometrial microbiota is not a carry-over from the vagina, because some bacterial genera present in the endometrium were not in the vagina of the same subject, and vice versa. Thus, although endometrial and vaginal microbiota were not statistically different in the pool of healthy and fertile women (P = .733) (Figure 3, B), we identified the existence of vaginal and endometrial bacterial communities that, although closely related in most of the subjects tested, are not identical in every woman; these differences were detected in approximately 20% of the women tested.
Figure 1Bacterial communities in endometrial and vaginal microbiota of fertile subjects
A, Shannon index rarefaction curves for each sample. Prereceptive (two days after the luteinizing hormone surge, LH+2). Receptive (seven days after the luteinizing hormone surge, LH+7). B, Principal coordinate analysis plot calculated based on Bray-Curtis distances.
EF, endometrial fluid; VA, vaginal aspirate.
Moreno et al. Endometrial microbiota impacts reproductive potential. Am J Obstet Gynecol 2016.
Number of pair samples with detection of most abundant operational taxonomic units in vaginal aspirate alone (VA), endometrial fluid (EF) alone, and in both VA and EF.
Moreno et al. Endometrial microbiota impacts reproductive potential. Am J Obstet Gynecol 2016.
After validating EF aspiration as an acceptable method to assess the structure of endometrial bacterial communities, EF was obtained from 22 fertile subjects during the acquisition of receptivity at LH+2 and LH+7 (total samples n = 44). A total of 166 different OTUs were identified. The most represented genus was Lactobacillus (71.7% of identified bacteria); while Gardnerella (12.6%), Bifidobacterium (3.7%), Streptococcus (3.2%), and Prevotella (0.866%) were the other most common genera. The bacterial communities found in EF samples from fertile subjects were clustered according to the bacterial OTUs identified and their abundances. The resulting heatmap showed 2 sets of samples classifying depending on the percentage of Lactobacillus OTUs identified (Figure 5). The first set of samples included those with a high abundance of Lactobacillus (>90%) and very low or nonexistent other OTUs. The second set of samples was formed by lower Lactobacillus abundances that coexisted with bacteria represented by other OTUs. According to the criteria used in a recent study in which the vaginal microbiota of pregnant women was analyzed during gestation,
the EF samples were classified in terms of the microbiota into 2 different groups: (1) a Lactobacillus dominated (LD)-microbiota for those samples in which >90% of the detected bacteria belonged to Lactobacillus OTUs, and (2) a non-LD (NLD) microbiota when <90% of the OTUs identified in the sample belonged to Lactobacillus OTUs and thus presented >10% of bacterial OTUs including pathogenic or dysbiotic bacteria. This classification is in agreement with previous evidence demonstrating that gonococcal adherence to in vitro cultured endometrial epithelial cells is significantly reduced at a 1:10 ratio (gonococci:lactobacilli).
and our classification of LD and NLD microbiota could be established by comparing CSTs 1, 2, 3, and 5 with the LD microbiota, and CST 4 with the NLD microbiota. This classification was used to identify the microbiota status of EF in subjects during the acquisition of endometrial receptivity, and in IVF subjects in the next pilot studies. Thus, of the 44 endometrial microbiota analyzed from fertile subjects, 28 were assigned to the LD group, and the remaining 16 to the NLD group. When the endometrial microbiota of IVF subjects’ samples were classified, 18 were assigned to the LD group and 20 to the NLD group; the remaining 3 did not produce sequencing data and were excluded from the analysis.
Figure 5Endometrial microbiota distribution during the acquisition of endometrial receptivity
Clustering of individual samples showed 2 groups depending on abundance of Lactobacillus OTUs. Technical filtering was performed on data produced by QIIME: ribosomal database project score <0.9 and ≤2 reads are filtered. All plots are based on filtered data. “Others” are filtered OTUs + remaining OTUs.
Moreno et al. Endometrial microbiota impacts reproductive potential. Am J Obstet Gynecol 2016.
Regulation of endometrial microbiota during the acquisition of endometrial receptivity
The endometrium is hormonally regulated throughout the menstrual cycle by ovarian steroids to induce the characteristics necessary for implantation and pregnancy. However, pyrosequencing data suggested a remarkable stability of the endometrial microbiota in the prereceptive vs the receptive phase (Figure 6, A, and Table S2). From the 22 subjects analyzed, 18 showed stable microbiota profiles during the transition from the prereceptive to the receptive phase (12 of them were LD, but 6 subjects had NLD microbiota). However, in 4 of the 22 subjects differences were observed during the acquisition of endometrial receptivity (from LD at LH+2 to NLD microbiota at LH+7 in 3 subjects, and from NLD at LH+2 to LD microbiota at LH+7 in 1 donor) (Figure 7). Interestingly, we found that bacterial community diversity did not vary significantly during the acquisition of endometrial receptivity in most cases (P = .221) (see PCoA plot in Figure 6, B), as shown by microbial taxa relative abundance and alpha diversity index, measured as Shannon value, in paired samples. Altogether, these results suggest that the endometrial microbiota is not hormonally regulated during the acquisition of endometrial receptivity, despite the tight hormonal regulation affecting endometrial epithelial cells within this period. Moreover, the endometrial microbiota profile of EF (defined as LD or NLD) in the prereceptive state coincided with that of the receptive phase in 81.8% of the cases (18 of 22 paired samples), but was variable over short time periods in a small number of subjects.
A, Shannon index rarefaction curves for each sample. B, Principal coordinate analysis plot calculated based on Bray-Curtis distances. Prereceptive (LH+2); Receptive (LH+7).
Moreno et al. Endometrial microbiota impacts reproductive potential. Am J Obstet Gynecol 2016.
Functional impact of the endometrial microbiota composition on reproductive outcome in patients undergoing IVF
We sought to determine the functional impact of different types of endometrial microbiota on reproductive outcome. EF samples (n = 41) were obtained from 35 subjects undergoing IVF just before collecting an endometrial biopsy for the diagnosis of endometrial receptivity using ERA. Genomic DNA was extracted from EF samples and subjected to pyrosequencing and bacterial taxonomical assignment (Table S3). Three of the 41 samples presented poor DNA quality and did not amplify properly. The bacterial communities found in EF samples were clustered according to the bacterial OTUs identified and their abundances. According to the groups observed in the clustering, 4 variables (percentage of Lactobacillus, Bifidobacterium, Gardnerella, and Streptococcus) were selected to predict the target classes named live birth or no live birth (including miscarriage and nonpregnancy). To classify the different samples, 2 supervised machine learning models were applied, a CART and a generalized linear model by logistic regression. Both models provided similar conclusions, ascribing the percentage of Lactobacillus as the only significant variable in these 2 models. In the case of CART, the rule obtained was: (1) if Lactobacillus percentage is ≥0.9 the classification is live birth; while (2) if Lactobacillus percentage is <0.9 the classification is no live birth. In the case of the logistic regression (after stepwise procedures) the probability of an IVF outcome resulting in live birth followed the equation: P (live birth) = exp(×)/[1 + exp(×)], where × = Ln(p/1 – p) = –2.359 + 2.554 * (percentage of Lactobacillus). The areas under the curve, using the original data for CART and logistic regression, were 0.76 and 0.75, respectively, showing that these 2 models could predict the pregnancy outcome based on relative abundance of Lactobacillus in EF. Then, based on these classifications, an endometrial microbiota profile (LD ≥ 90% Lactobacillus; NLD < 90% Lactobacillus) was assigned to each subject (Table 1). The comparison of our data with the existing literature suggested that the LD group in our cluster is comparable to CSTs 1-3 and 5 reported by others, while the groups dominated by Gardnerella, Streptococcus, and Bifidobacterium mostly resemble CST 4. For this reason, we consider that there are 2 general profiles depending on Lactobacillus dominance, LD and NLD. The NLD group could be subdivided depending on the different OTUs present, but due to the small number of patients investigated we preferred to group them as NLD (Figure 8).
Table 1Endometrial microbiota is related to pregnancy outcomes for in vitro fertilization patients
Sample
Endometrial receptivity (d)
Shannon index
Lactobacillus OTUs, %
Non-Lactobacillus OTUs, %
Unassigned, %
Endometrial microbiota
Pregnancy
Ongoing pregnancy
1
R (P+5)
3.090
90.85
5.35
3.80
LD
Yes
Yes
2
R (P+5)
3.079
95.10
2.13
2.77
LD
Yes
Yes
3
R (P+5)
4.257
66.48
31.28
2.24
NLD
Yes
Yes (VTOP)
4
R (P+5)
1.510
99.57
0.12
0.32
LD
Yes
Yes
5
R (P+7)
4.216
93.37
6.16
0.47
LD
Yes
Yes
6
R (CD21)
2.681
91.12
7.11
1.77
LD
Yes
Yes
7
R (LH+7)
NA
No data
NA
Yes
Yes
8
R (P+5)
NA
No data
NA
Yes
No
9
R (P+7)
1.550
3.27
94.36
2.37
NLD
Yes
No
10
R (P+5)
0.557
0.07
98.26
1.66
NLD
Yes
No
11
R (P+5)
1.316
0.26
85.43
14.31
NLD
Yes
Yes
12
R (LH+6)
1.527
99.54
0.32
0.15
LD
Yes
Yes
13
R (P+5)
3.288
97.35
2.40
0.25
LD
Yes
No
14
R (P+3.5)
5.834
36.11
58.24
5.65
NLD
Yes
No
15
R (P+5)
0.964
98.73
1.14
0.12
LD
Yes
Yes
16
R (CD21)
1.153
99.35
0.50
0.15
LD
Yes
No
17
R (CD20)
2.443
99.91
0.07
0.03
LD
Yes
Yes
18
R (P+6)
2.282
99.93
0.01
0.06
LD
Yes
Yes
19
R (P+5)
0.550
99.77
0.22
0.01
LD
Yes
Yes
20
R (P+4.5)
NA
No data
NA
Yes
Yes
21
R (P+5)
1.168
0.29
99.34
0.36
NLD
No
NA
22
R (P+5)
2.799
7.68
90.53
1.79
NLD
No
NA
23
R (P+5)
2.504
88.96
10.39
0.65
NLD
No
NA
24
R (P+5)
3.499
4.26
95.66
0.07
NLD
No
NA
25
R (P+5)
2.886
87.86
7.19
4.96
LD
No
NA
26
R (P+5)
4.954
3.43
95.31
1.26
NLD
No
NA
27
R (P+5)
1.900
92.70
5.74
1.55
LD
No
NA
28
R (P+5)
4.699
66.82
29.83
3.35
NLD
No
NA
29
R (P+7)
0.469
1.23
98.63
0.14
NLD
No
NA
30
R (CD22)
1.503
99.70
0.09
0.22
LD
No
NA
31
R (P+5)
4.639
73.89
23.81
2.30
NLD
No
NA
32
R (P+5)
1.927
4.62
95.22
0.16
NLD
No
NA
33
R (P+5)
0.805
98.00
1.96
0.05
LD
No
NA
34
R (P+5)
0.696
99.89
0.11
0
LD
No
NA
35
R (P+5)
3.202
87.70
12.20
0.11
NLD
No
NA
36
NR (P+5)
2.297
39.64
58.98
1.38
NLD
No ET
NA
37
NR (P+5)
5.274
16.34
82.92
0.74
NLD
No ET
NA
38
NR (P+5)
3.609
96.43
3.26
0.31
LD
No ET
NA
39
NR (P+3)
4.651
22.38
63.72
13.91
NLD
No ET
NA
40
NR (P+5)
4.026
14.07
84.30
1.63
NLD
No ET
NA
41
NR (P+4)
2.013
97.94
1.77
0.29
LD
No ET
NA
Shannon diversity values, OTUs abundances, and microbiotic profiles from in vitro fertilization patients. Technical filtering was done from data coming from QIIME: ribosomal database project score <0.9 and ≤2 reads are filtered. All plots are based on these filtered data.
The analysis of the resulting microbiota reflected significant differences in the bacterial diversity, with the NLD group showing higher diversity than those in the LD group, as assessed by Shannon diversity indexes (Figure 9). In contrast with those with LD microbiota, subjects with NLD microbiota had significantly lower implantation (60.7% vs 23.1%, P = .02), pregnancy (70.6% vs 33.3%, P = .03), ongoing pregnancy (58.8% vs 13.3%, P = .02), and live birth (58.8% vs 6.7%, P = .002) rates, as well as higher miscarriage rates, although this was not statistically significant (16.7% vs 60%, P = .07) (Figure 10, A, and Table 2). This adverse effect on pregnancy outcomes was more evident in subjects presenting high percentages of bacteria from the Gardnerella and Streptococcus genera (Figures 10, B, and 11). Bacterial community diversity did not correlate with IVF outcome (Figure 12).
Figure 9Community diversity in Lactobacillus dominated (LD) and non-LD (NLD) microbiota groups
A, Bacterial community diversity assessed by Shannon value, in endometrial fluid of in vitro fertilization patients classified as LD or NLD microbiota. Each individual is represented separately. Individual samples (gray dots). Mean of all subjects (red). B, Shannon index rarefaction curves show statistical significance (Shannon t test, P = .0239) of separation between 2 groups: LD (red) and NLD (blue).
Moreno et al. Endometrial microbiota impacts reproductive potential. Am J Obstet Gynecol 2016.
A, Bar charts showing individual microbial taxa composition and relative abundance of endometrial fluid samples of 35 IVF patients and their reproductive outcomes. B, Principal component analysis plot showing contribution of 20 most representative operational taxonomic units to reproductive outcome in IVF patients. *Voluntary termination of pregnancy.
Bar charts showing mean values of 20 most abundant operational taxonomic units (OTUs) in receptive subjects grouped by their reproductive outcomes: live births (LB) correspond to patients who became pregnant and successfully delivered; nonpregnant (NP) are patients who did not conceive; finally, miscarriage (MISC) applied for those patients who became pregnant but experienced either biochemical or clinical pregnancy. Twenty most abundant taxa are represented, while sum of remaining OTUs are included as “others” that comprise: Cloacibacterium, Ethanoligenens, Akkermansia, Acidimicrobiales, Cytophagaceae, Haliangiaceae, Halomonas, Hydrogenophaga, Brochothrix, Ureaplasma, Bilophila, Actinobaculum, Diaphorobacter, Phascolarctobacterium, SMB53, Oligella, WCHB1-84, Ellin6075, Cellvibrio, Balneimonas, Ochrobactrum, Petrobacter, Chryseobacterium, Carnobacterium, WAL_1855D, Thermoanaerobacterium, Anoxybacillus, Hymenobacter, Leucobacter, Eggerthella, Propionimicrobium, [Mogibacteriaceae], Ralstonia, 1-68, Rubrobacter, Methylobacterium, Brevibacterium, Pyramidobacter, Flavobacteriales, Pediococcus, Sphingopyxis, Alcanivorax, Clostridium [Lachnospiraceae], Parabacteroides, Neisseria, Peptoniphilus, Stenotrophomonas, Varibaculum, Novosphingobium, Pseudoramibacter_Eubacterium, Actinomyces, Butyricicoccus, Klebsiella, Sphingomonas, Lactobacillales, Acinetobacter, Dialister, Finegoldia, Lachnospira, Streptococcaceae, Anaerococcus, MND1, Micrococcus, Aerococcaceae, Owenweeksia, Halorhodospira, Anaerostipes, Bartonella, Coprococcus, Paracoccus, Fusobacterium, Bacteroidales, Peptostreptococcus, Erysipelotrichaceae, Swaminathania, Collinsella, Corynebacterium, Streptophyta, Prevotella, Staphylococcus, Rothia, Dorea, Oscillospira, Gemellaceae, Atopobium, S24-7, Ruminococcaceae.
Moreno et al. Endometrial microbiota impacts reproductive potential. Am J Obstet Gynecol 2016.
Bacterial community diversity and/or stability, assessed by Shannon value, in different groups analyzed. A, Receptive vs nonreceptive subjects diagnosed by endometrial receptivity array (ERA). B, Implantation rates among receptive subjects. C, Pregnancy outcomes on ERA-based receptive patients who became pregnant. Values from each subject are represented separately (gray diamonds). Red dots and line represent average values of subjects within group.
Moreno et al. Endometrial microbiota impacts reproductive potential. Am J Obstet Gynecol 2016.
Lactobacillus spp. present in the vagina produce lactic acid and short-chain fatty acids that decrease pH values (pH ≈ 4.5); thus, we hypothesized that a healthy endometrial microbiota, mainly comprising Lactobacillus spp., would produce lower pH values in EF samples compared to NLD microbiota. Therefore, the pH was measured in a series of EF samples (n = 14) before genomic DNA extraction. A high variability was found in the EF samples, with pH values between 6.6–8.51, independent of their bacterial composition and the percentage of Lactobacillus OTUs in those samples. Therefore, the pH of EF cannot be used as a predictor of endometrial microbiota status (Figure 13).
pH of endometrial fluid does not predict A, endometrial microbiota; B, implantation rates; and C, pregnancy outcomes for in vitro fertilization patients. pH from individual samples (gray diamonds). Red dots and line represent average values among compared groups.
Moreno et al. Endometrial microbiota impacts reproductive potential. Am J Obstet Gynecol 2016.
Finally, to evaluate the impact of the endometrial microbiota in IVF outcomes, a follow-up of those subjects previously diagnosed with nonreceptive endometrium (n = 5) was performed by assessing endometrial receptivity and endometrial microbiota simultaneously until their window of implantation was achieved. Only subjects showing endometrial receptivity and LD microbiota in the same cycle succeeded in their IVF treatment, while subjects with NLD microbiota presented adverse implantation rates and pregnancy outcomes despite receiving personalized embryo transfer (Table 3).
Table 3Follow-up of patients with nonreceptive endometrium
The uterine cavity has been traditionally considered to be sterile, but potentially susceptible to be affected by vaginal bacteria. The impact of BV in reproductive outcome remains controversial: 1 study correlated it with a decrease in pregnancy rates in IVF patients,
To our knowledge, this work is the first comparative study between endometrial and vaginal microbiota using next-generation sequencing. The results show that endometrial and vaginal microbiota can differ in structure and composition in some women. This finding supports the concept that the uterine cavity is not a sterile site, challenging the current dogma. Also, our results show evidences that NLD endometrial microbiota is associated to negative reproductive outcomes in IVF patients when compared to those with LD endometrial microbiota.
Is there an endometrial microbiota different that is from the vaginal microbiota?
The detection of bacterial DNA in 100% of EF samples is consistent with the identification of bacteria in 95% of the hysterectomy specimens analyzed by fingerprinting of the 16S rRNA gene for 12 bacterial species.
Our results also show that aspiration of EF under aseptic conditions is a safe and effective method to evaluate the endometrial microbiota. Further, these findings highlight the importance of endometrial investigation to improve pregnancy outcomes in those patients with differential vaginal and endometrial microbiota, since the bacterial structure and composition of the vagina does not accurately mirror, in every woman, the bacteria colonizing the endometrium, where embryonic implantation occurs.
Clinical implications
We also defined the endometrial microbiota profile as LD or NLD according to the identity and relative abundance of the bacteria identified in EF. This classification enabled the diagnosis of the endometrial microbiological health of IVF patients and its correlation with their reproductive outcome. A NLD microbiota strongly correlated with adverse outcomes, when compared to subjects presenting a LD endometrial microbiota. Interestingly, these correlations were much more evident when the non-Lactobacillus OTUs identified in the samples belonged to the Gardnerella or Streptococcus genera, as all subjects presenting high rates of these genera either did not become pregnant after embryo transfer or experienced a miscarriage. These results accord with previously published data of other groups that analyzed the impact of endometrial pathogens in IVF by using classic microbiological culture of the distal tip of the catheter used for embryo transfer.
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.
using a similar technical approach analyzing the transfer catheter tip instead of EF for bacterial 16S rRNA sequencing resulted in the identification of Lactobacillus as the most represented bacteria in endometrial samples. However, no association between Lactobacillus abundance and pregnancy outcome was shown in their IVF patients, which clearly differs with what is reported here.
The reason for this difference could reside in the quantitative dimension that we have introduced in our model to classify samples as LD or NLD depending on the percentage of Lactobacillus OTUs and that was not considered in the study by Franasiak et al.
Some authors suggest that the Lactobacillus genus produces lactic acid and short-chain fatty acids, acidifying the environment to pH ≤4.5 in the vagina and prohibiting the growth of other pathogenic or dysbiotic bacteria in healthy women.
Vaginal lactobacilli inhibiting growth of Gardnerella vaginalis, Mobiluncus and other bacterial species cultured from vaginal content of women with bacterial vaginosis.
Apparently, this is not the case in the endometrium because when pH levels were measured in EF samples, no correlation was observed between pH values and the endometrial microbiota, suggesting that other biochemical effects occur in the endometrium where the embryo will adhere and develop. In this sense, it is important to notice that NLD microbiota may trigger an inflammatory response in the endometrium that affects the success of embryo implantation, as inflammatory mediators are tightly regulated during the adhesion of the blastocyst to the epithelial endometrial wall.
Embryologic outcome and secretome profile of implanted blastocysts obtained after coculture in human endometrial epithelial cells versus the sequential system.
Cha J, Vilella F, Dey SK, Simón C. Molecular interplay in successful implantation. Science. S. Sanders. Washington; Nov. 12, 2013; Ten critical topics in reproductive medicine: 44-8.
Also, some other mechanisms of action related with the direct production of microbial metabolites and/or enzymes that are able to produce relevant compounds able to induce key cellular pathways in the endometrial epithelium need to be considered. In any case, a fascinating time for the study of the “dialogue” between the endometrial microbiota and the endometrial epithelium is beginning. These experiments will require new approaches most probably based on systems biology approaches.
Strengths and limitations
An important strength of our work is that endometrial receptivity was analyzed by ERA and embryo transfer performed in those subjects with receptive endometrium, avoiding any interference of the endometrial factor in this study. Additionally, in those subjects presenting a nonreceptive endometrium, a second sample of EF was obtained and analyzed until receptivity was confirmed. Only subjects who acquired endometrial receptivity with LD endometrial microbiota presented successful ART outcomes upon personalized embryo transfer.
A limitation of these prospective pilot studies is the time between EF collection and embryo transfer because the consistency of the endometrial microbiota in IVF subjects is unknown. Surprisingly, the endometrial microbiota is not regulated during the shift from the prereceptive (LH+2) to the receptive (LH+7) phase of the menstrual cycle in nearly 82% of the subjects when the endometrial microbiota is highly stable. This observation is in agreement with previous data reporting high stability of vaginal microbiome coinciding with the early and mid secretory phases in contrast with the high instability during the late secretory and menstrual phases of the cycle.
Another limitation is that, unless the molecular methods used in this work are widely accepted, no microbiological culture techniques were used in this study.
Conclusion
In conclusion, a human endometrial microbiota exists and is independent of hormonal regulation. The existence of non-Lactobacillus bacteria in the endometrium is correlated with negative impacts on reproductive function and should be considered as an emerging cause of implantation failure and pregnancy loss. The results presented herein expand the evaluation of endometrial receptivity not only at the morphological and molecular levels but also at the microbiological viewpoint. It is time to consider microorganisms not only as enemies but also as allies in reproductive medicine.
Acknowledgment
We thank Sheila M. Cherry, PhD, ELS, President and Senior Editor from Fresh Eyes Editing LLC, for her excellent work editing this manuscript, and Monica Clemente, PhD, for her valuable help with statistics.
Supplementary Methods
Subjects
For comparison between vaginal and endometrial bacterial communities, donors were fertile women recruited from the ovum donation program, aged 18–35 years, with normal body mass index of 19–29 kg/m2, normal karyotype, and regular menstrual cycles. Paired samples of endometrial fluid (EF) and vaginal aspirates were obtained 2 and 7 days after their LH surge within the same natural cycle. For the study of hormonal regulation of the endometrial microbiota, samples were also obtained from ovum donors at IVI Valencia, Spain, with the same inclusion criteria. EF samples were obtained within the same natural cycle two and seven days after the luteinizing hormone surge (LH+2 and LH+7, respectively) of fertile women from the ovum donation program at IVI Valencia. The functional impact of the endometrial microbiota on reproductive outcome was explored in infertile subjects undergoing in vitro fertilization treatment in whom a receptive endometrium was diagnosed by endometrial receptivity array (ERA) (Igenomix SL, Valencia, Spain). Inclusion criteria encompassed reproductive-age women ages 25-40 years, presenting normal body mass index of 19-29 kg/m2, normal karyotype, and intracytoplasmic sperm injection or oocyte donation treatments in whom at least 1 good-quality embryo was transferred. Some subjects with a nonreceptive endometrium result in the ERA test (n = 5) were tested again until receptivity was confirmed, and the EF microbiota was investigated. Personalized embryo transfer was performed according to ERA test results in all subjects. A general criterion in all studies excluded subjects who had used antibiotics or probiotics 1 month before the study.
EF aspiration
Briefly, with the patient in lithotomy position, the cervix was cleansed with a cotton swab and 20-80 μL of EF was aspirated using a catheter (Wallace, Smith Medical International Ashford, Reino Unido) transcervically introduced into the uterine cavity to avoid any contact with vaginal walls. To prevent contamination, cervical mucus was aspirated before EF recovery and suction was stopped at the entrance of the internal cervical os during catheter removal. The aspiration of EF is a painless and minimally invasive method that does not cause any risk for the patient and that can be safely used 24 hours before embryo transfer without altering implantation rates.
Therefore, the investigation of the endometrium does not increase the risk compared to vaginal investigation.
Endometrial receptivity diagnosis
Endometrial biopsies (∼3 g of tissue) from patients undergoing in vitro fertilization treatment were collected from the uterine fundus with the use of a Pipelle catheter (Genetics, Belgium) under sterile conditions. Total RNA was extracted by Trizol method according to the manufacturer’s protocol (Life Technologies Carlsbad, CA). RNA quality was assessed by loading samples into RNA Labchip and subsequently analyzed in an A2100 Bioanalyzer (Agilent Technologies). Sample preparation and hybridization were adapted from the Agilent technical manual (1 color). Briefly, first-strand cDNA was transcribed with T7-Oligo deoxythimine (dT) promoter primers. Samples were transcribed in vitro and Cyanine-3 labeled, all with the Low Input Quick Amp Labeling Lit (Agilent Technologies, Santa Clara, CA). The labeling reaction typically yielded 4–5 mg of complementary RNA with a specific activity >6. Fragmented complementary RNA samples were hybridized onto the customized ERA
by incubation at 65°C for 17 hours with constant rotation. After washing, hybridized microarrays were scanned in an Axon 4100A scanner (Molecular Devices, Sunnyvale, CA), and data were extracted with the use of Genepix Pro 6.0 software (Molecular Devices). Gene expression values were preprocessed and normalized, and subjected to the ERA computational predictor arrays.
The ERA test diagnoses the endometrial samples as receptive or nonreceptive with an associated diagnostic probability.
Genomic DNA isolation from EF and vaginal aspirate samples
Briefly, to obtain a complete digestion of the bacterial cell wall, an extra enzymatic lysis step was performed using 50 μL lysozyme (50 mg/mL) (Sigma, Dorset, United Kingdom) and bacteria lysis buffer (Roche, Madison, WI) with incubation at 37°C for 30 minutes. Subsequent steps (proteinase K, inactivation treatment, and purification) were performed according to manufacturer's protocol in a MagNa Pure compact (Roche). Total genomic DNA was measured using the Quant-iT PicoGreen DNA assay (Invitrogen) and photometric technology (Nanodrop, Waltham, MA).
Polymerase chain reaction and pyrosequencing
Polymerase chain reaction (PCR) was performed with 5 μL of DNA, 200 μmol/L each of the 4 deoxynucleotide triphosphates, 400 nmol/L of each primer, 2.5 U of FastStart HiFi polymerase (Roche, Madison, WI), 4% of 20 g/mL bovine serum albumin (Sigma), 0.5 mol/L betaine (Sigma), and the appropriate buffer with magnesium chloride supplied by the manufacturer (Roche). Thermal cycling consisted of initial denaturation at 94°C for 2 minutes followed by 30 cycles of denaturation at 94°C for 20 seconds, annealing at 50°C for 30 seconds, extension at 72°C for 1 minute, and final extension at 72°C for 5 minutes. To obtain the appropriate amount of material, reactions were repeated in triplicate and pooled by running the PCR amplicons on 1% (wt/vol) agarose gels. Amplicons were combined in a single tube in equimolar concentrations. The pooled amplicon mixture was purified twice (AMPure XP kit, Beckman Coulter, Brea, CA) and the cleaned pool requantified using Quant-iT PicoGreen DNA assay (Invitrogen, Carlsbad, CA). This pool was then diluted in TE buffer to 108 molecules/μL and PCR was performed.
Quality control of the FASTQ files
Quality control of the FASTQ files was perfomed using Fastx tool kit version 0.013
to remove reads with quality less than Q20, once the sequences were clean based on quality scores, we trimmed traces of the 16S rRNA primers and sequencing adapters using cutadapt version 1.2.
After primer removal, sequences with <300 nucleotides read length were trimmed using perl scripting. Clean FASTQ files were converted to FASTA files and UCHIME program version 7.0.1001
was used to remove chimeras that could arise during the amplification and sequencing step.
Receiver operating characteristic
A receiver operating characteristic (ROC) chart is a 2-dimensional plot with the proportion of false positives (1-specificity) on the horizontal axis and the proportion of true positives on the vertical axis (sensitivity) when using different cut-offs for a classifier score. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. A common measure for comparing the accuracy of various classifiers is the area under the ROC curve. It evaluates the method's ability to correctly classify. The closer to 1 the area under the ROC curve of a classifier is, the higher the accuracy. More details can be found in Fawcett.
Evaluation of the bacterial vaginal flora of 20 postmenopausal women by direct (Nugent score) and molecular (polymerase chain reaction and denaturing gradient gel electrophoresis) techniques.
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.
Vaginal lactobacilli inhibiting growth of Gardnerella vaginalis, Mobiluncus and other bacterial species cultured from vaginal content of women with bacterial vaginosis.
Embryologic outcome and secretome profile of implanted blastocysts obtained after coculture in human endometrial epithelial cells versus the sequential system.
Cha J, Vilella F, Dey SK, Simón C. Molecular interplay in successful implantation. Science. S. Sanders. Washington; Nov. 12, 2013; Ten critical topics in reproductive medicine: 44-8.
This work was equally supported by Igenomix SL and Lifesequencing SL. I.M. holds a Torres-Quevedo grant (PTQ-13-06133) from the Spanish Ministry of Economy and Competitiveness. F.V. was partially supported by Miguel Servet Program Type I Instituto de Salud Carlos III (CP13/00038), and Fondo de Investigación Sanitaria project (PI14/00545).
Disclosure: I.M., D.V., J.J-A., R.A., and C.S. are employed by Igenomix SL. J.F.M-B., F.M.C., and D.R. are employed by Lifesequencing SL. P.A. and J.R. are employed by IVI Valencia, and A.P. by IVI Rome. F.V. reports no conflict of interest.
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