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Das Kinderwunsch Institut Schenk GmbH, Am Sendergrund 11, Dobl 8143, AustriaMedical University of Graz, Institute of Human Genetics, Neue Stiftingtalstrasse 2, Graz 8010, Austria
Oral probiotic supplementation does not change vaginal alpha diversity
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Oral probiotics contain the relative abundance of Ureaplasma parvum
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L.crispatus, L.iners and L.gasseri were the leading vaginal representatives
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Oral probiotics may assist in limiting the growth of non-beneficial bacteria
Abstract
Research question
Does oral probiotic supplementation influence the relative abundance of different vaginal microbiota in women experiencing infertility?
Design
A prospective, monocentric randomized controlled trial. To study the influence of probiotics on infertility, 80 patients with primary or secondary infertility were included. Patients were assigned to either a probiotic treatment or a control group. Participants in the treatment group (n = 40) took one sachet (2 g) a day of a defined probiotic supplement limiting Lactobacillus strains. Patients in the control group did not receive any additional probiotic supplements. Vaginal samples were taken on day 20 of the menstrual cycle and 4 weeks later, on day 20, of the consecutive cycle. Subsequently, 16s rRNA gene analysis of the vaginal samples was conducted.
Results
After the intervention phase, no effects on alpha diversity resulting from treatment could be observed. The between sample diversity of different women (beta diversity) at baseline had no effects of age, treatment group or body mass index. Primary or secondary sterility, however, had a significant effect on community. Three clusters (Lactobacillus crispatus, Lactobacillus iners and Lactobacillus gasseri) were identified as the leading representatives. Furthermore, patients treated with probiotics showed limited growth of Ureaplasma parvum compared with the control group (P = 0.021).
Conclusions
This study points to a possible protective effect of probiotic supplements on the vaginal microbiota. It is tempting to speculate that this effect assists in containing the growth of non-beneficial bacteria and helps to prevent or cure a dysbiotic vaginal flora.
). Many circumstances have been identified to cause infertile conditions, with age being one of the most limiting factors of fertility. Besides acute, chronic or infectious diseases, environmental and occupational exposures, general lifestyle, genetic conditions and specific reproductive disorders can also affect either the man or woman attempting to conceive (
). In recent years, however, the microbiome has gained importance in the treatment of infertility and is considered an important player contributing to improved success rate of reproductive medicine treatments, such as IVF (
Within the female reproductive tract, the dominance of Lactobacilli is associated with a healthy vaginal microbial community in healthy women of reproductive age (
). In detail, certain community state types (CST), mainly CST I (Lactobacilli crispatus), CST II (Lactobacilli gasseri), CST III (Lactobacilli iners) and CST V (Lactobacilli jensenii) were found in non-pregnant, fertile and asymptomatic women from four ethnic groups (
). Lactobacillus strains produce lactic acid, causing an acidic environment of pH less than 4.5. This specific condition provides a highly protective environment against pathogens and is part of multiple defense mechanisms in the lower female genital tract (
). Furthermore, Lactobacillus species have been shown to produce antimicrobial bacteriocins to inhibit growth of undesirable species, such as Klebsiella species, Staphylococcus aureus, Escherichia coli or Enterococcus faecalis (
A higher abundance of Lactobacilli is associated with better reproductive outcomes; however, the presence of Enterococci, Enterobacteriaceae, Streptococci or gram-negative bacteria is related to lower implantation rates, decreased numbers of preterm birth and increased numbers of miscarriages (
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.
). The most common dysbiotic state is bacterial vaginosis, described as an anaerobic polybacterial state in the lower female genital tract. Because of the overgrowth of anaerobes, infections and obstetrical complications are triggered by noxious substances, such as polyamines and small polycationic molecules, with a wide array of biological functions that were recently shown to be involved in bacterial pathogenesis (
). Enhanced production of proteolytic enzymes that act on vaginal peptides to release polyamines cause a shift in the balance of cytokine expression towards pro-inflammatory cytokines, such as interleukin (IL)-1 β and IL-8 (
). Furthermore, bacterial lipoproteins adhere to and invade host cells of the genitourinary tract, trigger inflammation processes and lead to apoptosis and cell death (
). These changes in the vaginal environment increase the risk of acquiring sexually transmitted infections and affect women's reproductive health negatively. During pregnancy, bacterial vaginosis can lead to chorioamnionitis, preterm premature rupture of the membranes and preterm birth (
). As part of the mycoplasma family, Ureaplasma belong to the smallest self-replicating microorganisms and human parasites, and are the most potentially pathogenic bacteria in the human urogenital tract (
). Ureaplasma species do not possess a cell wall, and are surrounded only by a plasma membrane, contacting host cell surface via lipid-associated membrane proteins. Their pathogenicity is characterized by the production of proinflammatory cytokines as well as their ability to induce apoptosis in monocytes and macrophages (
). Ureaplasma colonization in the vagina is associated with infertility, stillbirth, histologic chorioamnionitis and neonatal morbidities, including congenital pneumonia, bronchopulmonary dysplasia, meningitis and perinatal death. Antibiotic resistance to macrolides, fluoroquinolones and tetracyclines has been reported (
). Hence, it has been suggested that Ureaplasma colonization should be diagnosed and treated early to prevent long-term infection and subsequent complications (
Despite the importance the microbiome has gained over recent years, its influence in IVF treatments is still controversial. Some studies suggest a negative influence of vaginal dysbiosis on pregnancy rates (
). Therefore, it is tempting to speculate that restoring a healthy vaginal microbiota is important in the treatment of women experiencing infertility.
One therapeutic approach to bacterial vaginosis is the administration of beneficial microorganisms (probiotics), mainly Lactobacillus species. The supplementation of exogenous Lactobacilli strains has been suggested as a cure for dysbiotic vaginal flora, reestablishing healthy conditions and improving female fertility health (
). A large number of clinical studies with oral supplementation have been conducted since then, for which a substantial number of literature reviews are available (
Studies on the effects of probiotic Lactobacillus mixture given orally on vaginal and rectal colonization and on parameters of vaginal health in women with intermediate vaginal flora.
). The main mechanism studied of how orally administered Lactobacilli settle in the vagina is their migration from the intestine to the anus and finally to the vagina. This mechanism seems to combine mucosa-adhesive features of Lactobacilli and chemotactic factors that guide their migration towards the vaginal mucosa (
). Although oral versus vaginal probiotic applications have numerous advantages and disadvantages, the biggest benefit of oral supplementation is its convenience for the patient and the concomitant compliance.
A recent meta-analysis has shown short- and long-term effects of probiotics in the treatment of bacterial vaginosis (
); however, the influence of probiotics on the abundance of unfavourable pathogens, such as U. parvum, has not been evaluated so far. Therefore, the aim of the present study was to investigate the effect of four probiotic strains (L. crispatus LBV88, Lactobacillus rhamnosus LBV96, L. gasseri LBV150N and L. jensenii LBV116) on the relative abundance of vaginal microbiota species in women experiencing infertility. We hypothesized that oral intake of probiotics may alter the vaginal microbiota, including the relative abundance of potentially pathogenic species, such as U. parvum.
Materials and methods
Sample collection
The study included 80 Austrian women experiencing unexplained primary or secondary infertility or male factor infertility during their first reproductive medicine treatment. All patients were aged between 18 and 40 years, with a body mass index (BMI) ranging from 19 to 29.9 kg/m2. Ethnicity was self-reported as white with European ancestry. Patients who met the following criteria were excluded: severe disease; acute disease, chronic disease, or both, e.g. acute vaginal infection; obesity; hirsutism; antibiotic intake (within the last 30 days before intervention); endometriosis; polycystic ovary syndrome; and use of other pre- or symbiotics. Patients were assigned to either treatment or control group, using block randomization to avoid confounding through treatment modalities. Participants in the treatment group (n = 40) took one sachet (2 g) of the probiotic OMNi-BiOTiC® FLORA plus+ (Institut Allergosan Pharm, Produkte Forschungs- u. Vertriebs GmbH, Graz, Austria) dissolved in 125 ml of water a day for a period of 4 weeks starting on day 20 of the menstrual cycle. The supplement is composed of four bacterial strains: L. crispatus LBV88, L. rhamnosus LBV96, L. gasseri LBV150N and L. jensenii LBV116. Patients in the control group did not receive any additional probiotic supplements. Vaginal samples were taken with sterile swabs (jeSwabTM) (Copan Diagnostics Inc., Murrieta, CA, USA) on day 20 of the menstrual cycle. Luteal phase was confirmed by transvaginal ultrasonography and by urinary LH. A second vaginal sample of every patient was obtained 4 weeks later, on day 20 of the consecutive cycle. Samples were stored at –72°C and 16s rRNA gene analysis was conducted (Biovis Diagnostik MVZ GmbH, Limburg an der Lahn, Germany).
During the trial, 19 patients were prematurely discontinued owing to antibiotic intake (n = 16), cancellation of fertility treatment (n = 2) or withdrawal from consent (n = 1). All samples and clinical records were collected at the fertility clinic Das Kinderwunsch Institut Schenk Gmb, Dobl, Austria, between 2018 and 2019. All participants consented to use of their medical records in research. The study was approved by the Ethics Committee of the Medical University of Graz, Austria (approval number: 30–182 ex 17/18; date of approval: 25 July 2018).
16s rRNA analysis and data processing
Data were generated from DNA extracted from the supplied vaginal swabs using the QIAamp UCP Pathogen mini kit automated on the QIAcube. The swab was transferred to a Pathogen Lysis Tube S filled with 0.65 ml ATL buffer (including Reagent DX) and incubated for 10 min at 56°C with continuous shaking at 600 revolutions per min. Subsequently, bead beating was carried out using a SpeedMill PLUS for 45 s at 60 Hz. Samples were then heated to 95°C for 5 min and centrifuged afterwards for 1 min at 10,000 revolutions per min. A total of 400 µl of the resulting supernatant was transferred to a 1.5 ml microcentrifuge tube, which was placed in the QIAcube for follow-up automated DNA isolation according to the manufacturer's protocol. Elution volume was 100 µl.
Amplification of the 16S rRNA gene, targeting the variable regions V1-V2 (27F-338R), was carried out. Sequencing was executed using the Illumina MiSeq (Illumina, Netherlands) using the Illumina V3 chemistry kit (https://www.illumina.com/products/by-type/sequencing-kits/cluster-gen-sequencing-reagents/miseq-reagent-kit-v3.html) to obtain 2 × 300 bp reads. Subsequent downstream processing of sequencing data was carried out using the DADA2 package (https://benjjneb.github.io/dada2/index.html, v1.10) for R (v3.6) using the following parameters in the filterAndTrim() function: truncLen=c(230,180), trimLeft=c(5, 5), maxN=0, maxEE=c(2,2), truncQ=5, rm.phix=TRUE. Amplicon sequence variants were taxonomically annotated using the Genome Taxonomy Database (GTDB; release r86; https://gtdb.ecogenomic.org/) using a Bayesian classifier. Good-quality data could be obtained for 29 samples from the probiotic treatment group and 30 samples from the control group. Two samples were discarded owing to low sequence counts, not recovered by sequencing twice, most likely because of low DNA yield from the original sample.
Statistical analysis
Unless otherwise stated, statistical tests were carried out in R (v3.6) using the R base package (R Foundation for Statistical Computing, Vienna, Austria; https://www.R-project.org/) default functions. Shannon species equivalent, a sound index sensitive to species richness, was used as measure for alpha (within-sample/community) diversity (
). Shannon diversity was calculated using the diversity() function of the vegan package for R (v.2.5-5; https://CRAN.R-project.org/package=vegan), followed by transformation using the exp() function. Baseline differences caused by primary and secondary sterility, and differences of treatment groups before treatment, were assessed using Wilcoxon Rank-Sum Test. Spearman's rank correlation test was used to assess the effects of BMI and age on alpha diversity. To assess differences in alpha diversity before and after intervention, paired Wilcoxon Rank-Sum test was used on data sets in the different treatment groups. Effects of BMI, age and cause of sterility, as well as differences between treatment groups on between-sample diversity of different women at baseline (beta diversity), were assessed using permutational multivariate analysis of variance using Bray–Curtis dissimilarity as implemented in the adonis() function of the vegan package for R. Unsupervised partitioning around medoids (PAM) clustering algorithm, using the pam() function of the cluster package for R (v.2.10; https://cran.r-project.org/web/packages/cluster/index.html), were used to assess potential clustering options. The main clustering candidates were selected based on Gini index. Community (in-)stabilities based on treatment were investigated by Fisher's exact test.
Results
Per sample community composition of cohort
Forty-four out of 59 samples showed community composition patterns that were characterized by dominance of Lactobacillus species (>50%) (Figure 1). The most abundant Lactobacillus species were L. crispatus (36% total, found in 41 out of 59 samples), L. iners (23% total, found in 16 out of 59 samples) and L. gasseri (10% total, found in 22 out of 59 samples). Six per cent of all Lactobacillus reads on genus level could not be allocated to subordinate species. Irrespective of Lactobacillus dominance, the most abundant bacteria not belonging to the Lactobacillus genus were Olsenella, which accounted for 3.38% of total community composition and were found in 18 out of 59 samples. Fifteen out of 59 samples were not dominated by members of the Lactobacillus genus but were also not dominated by any other genus except one (sample 55). Among these 15 samples, the most abundant bacteria belonged to the genus Olsenella and Prevotella. Three samples yielded reads that mostly could not be allocated to specific genera or species.
Figure 1Overview of per sample community composition of the cohort before the intervention. Different colours represent different classes, genera and species.
Shannon species equivalent was used as measure for alpha (within-sample/community) (Figure 2a) diversity. Baseline differences caused by primary and secondary sterility, and differences of groups before treatment, yielded no significant differences (Figure 2b). Neither BMI (Figure 2c) nor age (Figure 2d) showed significant effects on alpha diversity. No significant changes in alpha diversity before and after intervention could be identified, independent of treatment (Figure 3).
Figure 2Alpha (within-sample/community) diversity measured by Shannon species equivalent before the intervention in connection with (A) intervention arm; (B) reason for sterility; (C) body mass index (BMI); and (D) age.
Figure 3Paired Wilcoxon Rank-Sum test comparison of alpha diversity between timepoints separated by treatment. MB01, timepoint before intervention; MB02, timepoint after intervention.
Effects of BMI, age and cause of sterility, as well as differences between groups on between-sample diversity of different women at baseline (beta diversity), were assessed. Sterility group (primary or secondary) seemed to affect community composition significantly (R² = 0.05026; P = 0.017) (Figure 4). No effects could be identified for BMI (explained variance R² = 0.015; P = 0.712), age (R² = 0.0096; P = 0.760) (data not shown) and treatment group (R² = 0.0071; P = 0.893) (Figure 5).
Figure 4Multidimensional scaling (MDS) plot of Bray–Curtis dissimilarities. Each blue circle and red triangle represent one individual with primary and secondary infertility, respectively. Group centroids are marked with crosses of the groups’ respective colours. Ellipses represent 95% confidence intervals of the groups’ multivariate t-distribution.
Figure 5Cluster analysis. (A) Calinsky–Harabasz index of unsupervised partitioning around medoids. Orange bar = best result; (B) Gini index of bacterial species with largest influence on cluster membership; (C) multidimensional scaling (MDS) plot of Bray–Curtis dissimilarities. Samples are coloured by cluster membership. High confidence (>0.5), filled shapes; low confidence (<0.5), empty shapes.
In advance of the between-sample analyses after treatment, potential clustering options were assessed to screen for possible clustering dynamics that would occur under treatment, i.e. significant change of cluster membership. The PAM clustering algorithm showed best results (Calinsky–Harabasz index) for a clustering into three clusters (Figure 6a). The most important features for cluster discrimination (based on Gini index) (Figure 6b) were the three Lactobacillus species: L. crispatus, L. iners and L. gasseri. A leave-one-out approach was used for Random Forest: 51 out of 59 (86%) individuals could be assigned to one of the clusters with high confidence (high confidence [>0.5], filled shapes; low confidence [<0.5], empty shapes) (Figure 6b). Intra-individual comparisons in Bray–Curtis dissimilarity before and after the intervention period in connection with intervention and Lactobacillus cluster membership before intervention are presented in Figure 6c and Figure 6d, respectively.
Figure 6(A) Changes in beta (inter-sample) diversity as measured by Bray–Curtis dissimilarity before (circles) and after (triangles) the intervention period in the intervention and control groups; and (B) stability of Lactobacillus cluster membership. Filled shapes represent sample communities that could be assigned to one of the cluster with high confidence (>50%), samples from the same individual are connected by dashed lines. Intra-individual comparisons in Bray–Curtis dissimilarity before and after the intervention period in connection with (C) intervention arm and (D) Lactobacillus cluster membership before intervention. MB01, timepoint before intervention; MB02, timepoint after intervention.TP1, time period 1; TP2, time period 2.
Most of the samples had a stable cluster membership (Figure 7). Six samples in total transferred to a different cluster assignment after the intervention phase, four of which belonged to the probiotic treatment group (two from cluster 1 to cluster 3 and vice versa) and two from the control group (one from cluster 2 to cluster 3 and one from cluster 3 to cluster 1). No statistical effects on community (in-)stability could be identified based on treatment (P = 0.4) (Fisher's exact test).
Figure 7Between timepoint analysis of community stability of the whole cohort. MB01, timepoint before intervention; MB02, timepoint after intervention
In accordance with the findings of the clustering dynamics analysis, direct comparison of relative abundances of individual species (where possible) or genera revealed that most representatives retained stable relative amounts irrespective of the comparison between time point or intervention (Figure 8). Exceptions with nominal statistical significance were the decrease of unclassified members of the genus Clostridium over time (mean abundance T1 = 0.815%; T2 = 0.048%, P = 0.011) in the control group, whereas it remained unchanged in the probiotics group. Obtained sequencing data did not yield sufficient information to provide higher-level resolution within the class as well as group difference in the relative abundance of the species U. parvum between control (3.52%) and intervention group (0.77%, P = 0.021) after the intervention period (Figure 9).
Figure 8Overview of feature changes between (A) timepoints and (B) treatments. MB01, timepoint before intervention; MB02, timepoint after intervention
Figure 9Between timepoint comparison of relative abundances of Ureaplasma parvum, separated by placebo or treatment. MB01, timepoint before intervention; MB02, timepoint after intervention
A pairwise comparison between timepoints within groups did not reveal a significant change in any of the groups (P = 0.22 [control], P = 0.75 [probiotics]).
Discussion
In the present study, we showed that oral probiotics did not influence alpha or beta diversity of the vaginal microbiota in women experiencing unexplained infertility. Patients treated with probiotics, however, showed a contained relative abundance of U. parvum after the intervention period compared with the control group, indicating a possible protective effect of the natural microbiota caused by probiotics. To the best of our knowledge, this is the first study showing a possible direct influence of probiotics on the relative abundance of pathogenic bacterial strains.
The microbial composition of the female reproductive system has been intensively reviewed (
). The upper female reproductive system was considered a sterile niche for some time. Recent evidence, however, suggests the presence of microorganisms in the fallopian tubes, the uterus and ovaries (
). The question of how microbiota maintain vaginal health and influence immune function is currently under investigation. During fertilization, cervical bacteria enter the uterus by spermatozoa and interact with the oocyte in the beginning of embryo development. This step could be critical for fertility, as gestational infections reduce fertility and increase the possibility of preterm birth and miscarriage (
In the present study, 44 out of 59 samples showed community composition patterns that were characterized by dominance of Lactobacillus species and thus resembled composition patterns previously described (
Temporal and spatial variation of the human microbiota during pregnancy.
Proceedings of the National Academy of Sciences of the United States of America.2015; 112 (https://doi.org/): 11060-11065https://doi.org/10.1073/pnas.1502875112
). The most abundant Lactobacillus species were L. crispatus (CST I), L. gasseri (CST II) and L. iners (CST III), reflecting the bacterial CST introduced by
. In this study, five CST were identified, depending on ethnic and biographical background. Four of the CST, however, are dominated by Lactobacillus species (L. crispatus, L. gasseri, L. iners,L. jensenii) and are associated with healthy women. The fifth CST is dominated by various anaerobic bacteria, associated with increased risk of sexually transmitted infections and preterm birth (
Besides the presence of Lactobacillus species, age, BMI, and primary and secondary infertility have been thought to influence bacterial composition in the vaginal microbiota. In the present study, neither BMI nor age showed significant effects on alpha or beta diversity before and after intervention. Only primary versus secondary infertility seemed to affect community composition before probiotic treatment significantly, which, to the best of our knowledge, has not yet been reported. Only a few studies have focused on the postpartum maternal microbiome, revealing the importance of biological and environmental influences, such as sleep deprivation or dietary changes on the commensal bacteria within the body (
). Furthermore, a dramatic change in the vaginal microbiome after delivery was reported, displaying a less dominant Lactobacillus species dominant flora with a higher alpha diversity independent of ethnicity (
). It is tempting to speculate that these changes may hamper the chance of a consecutive pregnancy in women with secondary infertility, and certainly warrants further research in this patient group.
In general, the vaginal microbiota is remarkably stable in its composition. Variations in aerobic and anaerobic communities during the menstrual cycle have been observed (
). Deviations from the microbial stability correlate with time in the menstrual cycle, and may be influenced by bacterial community composition and sexual activity (
). During menses in particular, a temporary instability of the vaginal flora has been described, associated with a decreased relative abundance of Lactobacillus species and a higher concentration of non-Lactobacillus species (
). Our data suggest a stable cluster membership on day 20 of the menstrual cycle in women experiencing unexplained infertility, independent of probiotic treatment. Three clusters were discriminated owing to the relative abundance of the three Lactobacillus species (L. crispatus, L. iners and L. gasseri). Six samples in total transferred to a different cluster assignment after the intervention phase. No statistical effects on community (in-)stability could be identified based on treatment. Interestingly, we observed slight community variations in the Clostridium genus. We identified a decrease of unclassified members of Clostridium over time in the control group, which may be a result of the described given variability.
As previously mentioned, Lactobacillus species are thought to play a critical role; however, studies have shown that a healthy vaginal microbiome is not necessarily Lactobacillus dominated. Factors, such as ethnicities, contraceptive use and sexual behaviour, also contribute to the overall vaginal environment. A study of South African women found that only a minority (37%) had a Lactobacillus dominant vaginal microbiota (
). Findings like this challenge the traditional understanding of a healthy vaginal microbiome and require further investigation. Despite these novel findings, much evidence in reproductive medicine shows that a non-Lactobacillus dominant microbial flora is associated with lower implantation rates, pregnancy rates and live birth rates (
). Hence, it is important to maintain a healthy vaginal microbiota to avoid dysbiosis states, such as bacterial vaginosis. Bacterial vaginosis directly affects fertility and reproductive health, as an ascending dissemination of the involved pathogenic species leads to tubal infertility and increases the risk of acquiring sexually transmitted diseases (
). Yet, it has been related to implantation problems and miscarriages, and has been detected in the vaginal flora of women diagnosed with infertility (
A retrospective pilot study to determine whether the reproductive tract microbiota differs between women with a history of infertility and fertile women.
). Increased abundance of U. parvum are strongly correlated with a higher risk for spontaneous preterm birth, low birth weight and bronchopulmonary disease in the preterm neonate (
). Studies have shown that timing and duration of fetal exposure to Ureaplasma, and the fetal and maternal inflammatory response, play a key role for neonatal outcomes. Interestingly, the rate of Ureaplasma respiratory tract colonization increases with duration of rupture of membranes, indicating that Ureaplasma is vertically transmitted from mother to the child as a result of an ascending infection from the lower genital tract (
Maternal genital colonization with Ureaplasma urealyticum promotes preterm delivery: association of the respiratory colonization of premature infants with chronic lung disease and increased mortality.
In the present study, we recognize a significant difference in the abundance of U. parvum between the probiotic group and the control group. In the probiotic group, the relative abundance of U. parvum remains low, whereas, in the control group, we report an increase of U. parvum abundance. Direct comparison of U. parvum abundance shows that the significant difference between groups is caused by an increase of relative abundance in the non-treated control individuals, rather than a reduction in the probiotic treatment group. It is tempting to speculate that probiotics have a protective effect by temporarily hindering pathogenic strains to disseminate. The interaction of Lactobacilli and Ureaplasma has been studied recently by
. They were able to demonstrate that Lactobacilli inhibited the death of vaginal epithelial cells after incubation with U. parvum and suggested their potential for maintaining a healthy vaginal environment, in line with our findings. One possible protective mechanism of Lactobacilli in the study by
was competitive adhesion to host epithelial cells. The adhesion capacity and hydrophobicity of the probiotic strains suggested antagonistic effects on the adhesion of lipid-associated membrane proteins to the plasma membrane of the vaginal epithelium. This high hydrophobicity, coupled with the self-aggregation ability, may cause the lipid-associated membrane proteins to bind to the cell wall of these microorganisms rather than to the epithelial cell membrane (
). In addition, many antagonist effects of probiotic microorganism were described, such as competitive adhesion to the mucosa and epithelium; strengthening of the epithelial barrier; secretion of antibiotics substances, such as bacteriocins and organic acids; and modulation of the immune system (
The use of probiotics as a treatment for patients with dysbisis has been under debate for some time. As antibiotic treatments do not completely eradicate vaginal biofilms and negatively influence the existence of healthy vaginal microbiota (
), probiotics seem to be a good alternative or additional treatment option for patients suffering from vaginal dysbiosis. Numerous meta-analyses, however, have concluded that probiotics only slightly enhance the temporary cure rate of dysbiotic states caused by bacterial vaginosis and vulvovaginal candidiasis and not changing alpha or beta diversity (
The small sample size may be considered a study limitation; however, sound statistical analysis could be conducted for all analysed parameters. Furthermore, microbiota analysis was based on the amplification of variable regions of the 16S rRNA gene. These regions have been a mainstay of sequence-based bacterial analysis. Although high-throughput sequencing of the full genome would reveal additional information, the method used in the present study is considered convenient and powerful to infer likely taxonomy from the generated data (
). In addition, the containment of U. parvum abundance is of nominal significance; therefore, a larger dataset in future studies should be envisaged to confirm this finding. The cause of infertility of the patients has not been considered, which may be seen as drawback of the study. Patients with polycystic ovary syndrome or endometriosis, both known to affect the microbiome in the reproductive tract, however, were excluded (
). Therefore, we created a female study population not affected by any obvious fertility issue and diagnosed with unexplained infertility or male factor infertility. The heterogeneity in study designs, especially in the composition of probiotic compounds, makes it difficult to achieve a consensus for probiotic administration. Therefore, future studies should attempt to adjust study designs to make results more comprehensible and comparable.
In conclusion, our results clearly show that probiotics do not influence alpha or beta diversity of the vaginal microbiome. Patients treated with probiotics showed contained growth of U. parvum compared with the control group. These results may provide novel insights into the interaction of Lactobacillus species and U. parvum. It is tempting to speculate that probiotics, especially Lactobacillus species, could produce a temporary protective effect of the vaginal microbiota by containing or suppressing non-beneficial bacteria, such as U. parvum. Further research is warranted, and professional medical associations should issue well-defined recommendations on the use of probiotics in gynaecological disorders, which may lead to novel approaches in infertility therapy.
Acknowledgements
The authors would like to thank the team at Das Kinderwunsch Institut Schenk GmbH for their valuable help and expertise, especially Cristina Pastor Leary for spelling and grammar check. The authors thank Dr Malte Rühlemann from the Institute of Clinical Molecular Biology (IKMB), Kiel University, Germany, for the bioinformatical analyses as well as sound statistical evaluation of the acquired datasets.
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Cervicovaginal Bacteria Are a Major Modulator of Host Inflammatory Responses in the.
Temporal and spatial variation of the human microbiota during pregnancy.
Proceedings of the National Academy of Sciences of the United States of America.2015; 112 (https://doi.org/): 11060-11065https://doi.org/10.1073/pnas.1502875112
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.
Maternal genital colonization with Ureaplasma urealyticum promotes preterm delivery: association of the respiratory colonization of premature infants with chronic lung disease and increased mortality.
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A retrospective pilot study to determine whether the reproductive tract microbiota differs between women with a history of infertility and fertile women.
Michael Schenk is Medical director of the Kinderwunsch Institut Schenk GmbH fertility clinic in Dobl, Austria. As a gynaecologist, he is constantly challenged to improve the techniques of IVF. His research interests are predominantly related to biomarkers in fertility treatment as well as preimplantation genetics and early implantation events.
Key message
Probiotics did not change vaginal alpha diversity but contained relative abundances of Ureaplasma parvum compared with the control group. It is tempting to speculate that probiotics have the effect of protecting the vaginal microbiota by limiting non-beneficial bacteria.
Article info
Publication history
Published online: June 13, 2021
Accepted:
June 4,
2021
Received in revised form:
May 25,
2021
Received:
December 21,
2020
Declaration: GW and NR are direct employees of Das Kinderwunsch Institut Schenk GmbH. LG and JS are employees of Institut Allergosan. MS was provided study medication and sample analysis by Institut Allergosan.
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