Interaction of genital microbiota in infertile couples

Interaction of genital microbiota in infertile couples | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Interaction of genital microbiota in infertile couples David Baud, Adriana Peric, Angela Vidal, Jürgen M. Weiss, Philipp Engel, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3063243/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Bacteria colonise most of the human body and the genital tract is not an exception. While it has been known for decades that a vaginal microbiota exists, other genital sites have traditionally been viewed as sterile environments, with bacterial presence associated only with pathological conditions. However, recent studies identified specific patterns of bacterial colonisation in most genital sites. Shifts in the bacterial colonisation of the female genital tract have been linked to impairment of reproduction and adverse pregnancy outcomes, such as preterm birth. The goal of this project is to understand the association between the genital microbiota of couples seeking assisted procreation aid and the outcome of this treatment. Male and female partners were considered as a unit (“couple microbiota”) and the interaction between their microbiota will be evaluated. Results We have characterised microbial samples coming from vaginal and penile swabs, as well as follicular fluid and semen, using next generation sequencing (16S rRNA profiling). The results revealed variability in bacterial biomass across different sample types, with Lactobacillus spp . dominating in vaginal and follicular fluid samples. Male samples exhibited higher diversity and harboured bacterial genera previously associated with negative obstetrical and gynecological outcomes. In addition, we found evidence of inter-partner microbiota interaction, indicating possible bacterial transmission between partners. Conclusions With this project, we aimed to gain a better understanding of how the male genital microbiota could influence the lower (vagina) and upper (follicular fluid) female genital tracts. Our results suggest a very limited impact of male microbiota on the female bacterial colonisation, although the information about the sexual activity of the couples involved in the study was missing. Future research should focus on understanding the influence of sexual activity on microbial composition and stability in different genital sites, especially in the case of infertile couples. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Bacteria and other microorganisms colonise most of the ecological niches present in the human body [ 1 – 3 ]. They are not only passive commensals but have a profound influence on the host’s homeostasis, playing a significant role at multiple levels, including protection against pathogens, maturation of the immune system, metabolic pathways, vitamin synthesis, among many others [ 1 , 3 , 4 ]. Thus, it is not surprising that a disbalance or dysbiosis of the microbiota has been associated with several adverse outcomes. The impact of the microbiota on the genital tract is not an exception and an increasing number of studies exploring its role on pregnancy, infertility and adverse outcomes are being carried out [ 5 – 7 ]. Most studies focus on vaginal microbiota, which in a healthy state is dominated by members of the Lactobacillus genus [ 8 ]. Nevertheless, the colonisation pattern may change over time [ 9 ]. During the early stages of life, vaginal bacteria form low abundant and highly diverse communities [ 10 ]. During puberty, under the influence of sexual hormones, the vagina undergoes to important physiological changes, including the thickening of the epithelium and an increase in the production of glycogen, a condition that favours the proliferation of lactobacilli. Again, with hormonal changes during menopause, the proportion of lactobacilli diminish, leading to a more diverse and less abundant microbiota [ 11 ]. Variation of the composition of the vaginal microbiota may also occur at a shorter time scale and may be influenced by multiple factors. Studies on different human population suggest that the genetic background plays an important role in the stability of colonisation by lactobacilli, probably by the interplay of the host’s immune response [ 12 , 13 ]. Women of Caucasian and Asian origin tend to have a more stable, i.e. Lactobacillus -dominated, microbiota compared to women of African and Hispanic origin [ 12 ]. Recently, bacterial colonisation of other parts of the female genital tract has been characterised [ 14 ]. While previously considered sterile, the existence of specific microbial colonisation profiles has been described for the uterus, fallopian tubes and ovaries [ 14 ]. Several studies published in recent years, support the notion that a low-biomass microbiota is present in the uterine cavity and the rest of the upper genital tract, forming a continuum with the lower genital tract [ 14 – 16 ]. Compared to vaginal microbiota, there is an increased bacterial diversity, but there is still no clear evidence of the exact impact of the upper tract microbiota on health and reproductive outcome [ 14 ]. Increase in the diversity of vaginal bacteria (mostly anaerobic or facultative anaerobic bacteria) is associated with adverse gynecological and obstetrical outcomes. Bacteria that have been associated with a negative impact include, amongst others, Chlamydia trachomatis , Gardnerella vaginalis , Prevotella spp. , Sneathia spp ., Bacteroides spp ., Mobiluncus spp. and Atopobium vaginae [ 17 – 20 ]. This list is certainly not exhaustive as many other species might be associated with vaginal infections [ 21 ]. Women with a dysbiotic vaginal microbiota have an increased risk of developing both gynecological (cancer) and obstetrical (miscarriage, preterm birth) pathologies [ 17 , 22 ]. Therefore, unlike other body sites, increased microbiota diversity is negatively associated with gynecological and obstetrical outcomes [ 23 , 24 ]. However, the pathophysiological mechanisms remain unknown as different combinations of genital bacteria can form the same pathology, as it is the case in bacterial vaginosis. Other than vaginal colonisation, microbiota of the female upper genital has not been extensively investigated, compared to other body sites. Similarly, the male genital microbiota has been mostly neglected. Initial studies on the colonisation of the male genital tract focused on well-known genital pathogens ( Chlamydia trachomatis , Ureaplasma spp. and Mycoplasma spp. , among others) or have relied on classical microbiological methods for culturing bacteria [ 25 – 28 ]. This led to the general view that semen is poorly colonised by bacteria, except in cases of ongoing infections where pathogens directly impair fertility. In recent years, bacterial communities colonising the male genital tract have been characterised using next generation sequencing [ 29 – 31 ]. It became clear that semen is not sterile and that bacteria are found not only in infertile men, but also in normozoospermic men (men with normal sperm parameters according to reference values set by the WHO, which include spermatozoa count, concentration, motility and morphology). Most of the studies have been performed on male partners of infertile couples, due to the availability of semen samples that were used for spermiogram analysis [ 32 – 35 ]. Despite multiple sequencing strategies, analysis pipelines and cohorts, similar community types were characterised, suggesting that a specific seminal microbiota might be necessary for optimal sperm function (or that an inadequate seminal microbiota may interfere negatively with sperm function). Presence of lactobacilli was often associated with good semen parameters, but their exact role remains to be elucidated. On the other hand, many bacterial genera found in semen were previously negatively associated with vaginal health and reports indicate that in some cases, sexual intercourse could lead to disturbed vaginal microbiota [ 36 , 37 ]. As an example, Prevotella is one of the major genera encountered in semen and has been previously associated with bacterial vaginosis [ 17 ]. Interestingly, an increased abundance of Prevotella spp. was also negatively associated with semen parameters such as motility and morphology [ 29 , 31 ]. Other semen-associated genera that may negatively impact female genital tract include, amongst others, genera like Gardnerella , Atopobium , Sneathia , Dialister and Finegoldia [ 29 , 31 ]. Despite the potential impact of seminal bacteria on human reproduction and dysbiosis of the female genital tract, this field of research remains understudied. In addition to investigating the composition of genital microbiota in infertile couples and its potential impact on infertility, our study also aimed to explore the potential interaction between male and female microbiota. By collecting samples from both male and female partners, we aimed to investigate, whether the microbiota of one partner could influence the composition of the other partner's microbiota. Due to the potential issues associated with analyses involving low biomass microbiota, like semen and follicular fluid samples, we implemented a series of negative controls and stringent in silico elimination of possible contaminants to ensure the accuracy and reliability of our results. Materials and methods Patients and samples collection This study comprises samples from couples diagnosed with infertility that were obtained from the Luzern Cantonal Hospital (Switzerland) between October 2018 and July 2020. All patients included in the study gave their written consent and utilization of the samples was approved by the Ethics Committee Northwest and Central Switzerland (EKNZ - REPROLUKS003), according to the Swiss Federal Act on Research involving Human Beings. Samples were collected using eSwab (Copan Diagnostics, Italy) by direct sampling (vagina and penis glans) or by immersion in the biological fluid (follicular fluid and semen). Negative controls consisted of swabs that were opened in the same rooms in which the medical examination took part or processing of sterile water through the pump used to retrieve oocytes and follicular fluid (Table 1 ). Table 1 – Description of the sample types Sample (abbreviation) Description and sampling Vagina (vag) Vaginal swab Follicular fluid (fol) Immersion of the swab in the follicular fluid obtained from oocyte retrieval Penis (pen) Swab of the penis glans (self-sampling) Semen (spe) Immersion of the swab in semen sample obtained for spermiogram analysis Control (ctrl) Swab exposed to the environment in which sampling was performed Follicular fluid control (fol_ctrl) Immersion of the swab in the sterile medium that has been passed in the aspiration device used for follicle retrieval Technical control (tech_control) Sterile water sample that has been processed for DNA extraction Water (water) Sterile water sample used as negative control for the amplification of the V1-V2 regions of the 16S rRNA Community DNA Standard (comm) Commercial Microbial Community DNA Standard ZymoBIOMICS Microbial Community DNA Standard (Zymo Research Corp., Irvine, CA, United States) was used to evaluate the potential variability of the sequencing results between different runs. DNA extraction and quantification of bacterial copy number Tubes containing swabs were vortexed for 30 sec. A total of 500 µl of the tube medium was used for DNA extraction, which was performed with the QIAamp DNA mini kit (Qiagen AG, Basel, Switzerland). After an initial step of lysis using 0.3 µl Ready-Lyse Lysozyme (Epicentre, Madison, WI, USA) at 37°C with 500 rpm shaking for 60 min, extraction was done according to manufacturer’s specifications. Elution was done with 50 µl molecular biology grade H 2 O. Quantification of bacterial 16S rRNA copy number was assessed by quantitative RT-PCR using 300 nM of both primers F-tot (5′-GCAGGCCTAACACATGCAAGTC-3′) and R-tot (5′-CTGCTGCCTCCCGTAGGAGT-3′) in the Rotor Gene 6000 thermocycler (Corbett Research, Sydney, Australia) with the iTaq Universal SYBR Green Supermix (Bio-Rad, Reinach, Switzerland). Bacterial 16S rRNA amplicon sequencing The variable regions V1-V2 of the 16S rRNA gene were amplified using custom barcoded primers (F-27/R-338) containing Illumina sequencing adapters, as previously described[ 38 ]. PCR was done with the Kapa HiFi PCR kit (KAPA Biosystems, Cape Town, South Africa) using 5 µl of extracted DNA as template and the following cycling conditions: 3 min initial denaturation at 95°C, followed by 30 cycles of 30 s at 98°C, 30 s at 56°C and 1 min 30 s at 72°C, with a final extension of 5 min at 72°C. Four libraries were prepared for a total of 282 samples, including the negative controls. Prior sequencing, PCR product of each sample were evaluated by agarose gel electrophoresis (supplementary Fig. 1). Based on gel band intensities, samples were classified as “high” (H, strong band), “low” (L, visible band) and “null” (N, absence of a visible band). For each of the two libraries, 3 pools of samples corresponding gel band intensities were prepared (H, L and N). Each pool was purified and quantified, in order to maximise the number of reads for each sample. This step was necessary to avoid the overrepresentation of reads coming from the samples with high bacterial loads ( i.e. , most of the vaginal samples and a small proportion of penis samples). Illumina sequencing was performed at the Lausanne Genomic Technologies Facility (GTF) of the Lausanne University using an Illumina MiSeq instrument in paired-end mode 2 x 250 nt (Illumina, San Diego, CA, United Sates). Bacterial community analysis Pre-processing and quality filtering Demultiplexing of the raw sequencing data was performed with the illumina-utils package [ 39 ]. Read processing and phylogenetic sequencing analysis was performed with the DADA2 pipeline using default parameters [ 40 ] (Supplementary Data, GitHub pipeline). Read quality control, trimming, dereplication and filtering were also perform using the DADA2 pipeline. Samples with less than 1000 reads post processing were removed from the analysis (Fig. 1 C, kept vs removed samples). Taxonomic assignment and phylogeny Taxonomy was assigned after alignment with SINA using the SILVA 16S rRNA database (SILVA NR v132) [ 41 ], resulting in 16S rRNA gene amplicon sequencing variants (ASVs). Phylogenetic tree was constructed using FastTree version 2.1.10. Decontamination and normalization Relative abundance of ASVs were converted to absolute abundances by multiplying the 16S rDNA gene copy quantified by qPCR in each sample. Decontamination of sequencing data was performed with the “decontam” package in R [ 42 ]. The combined mode, in which both frequency and prevalence methods were used to identify putative contaminants, was used with the default settings. Microbiota diversity analyses Alpha diversity analyses were performed using the phyloseq function plot_richness() , which uses the “vegan” package in R. Results were plotted and analysis of variance (ANOVA) was performed to calculate significant differences between sample types and other metadata variables. Paired sample wilcoxon test was used to investigate differences when categorized into sample type. To explore the beta diversity the unweighted and weighted UniFrac distances between samples using the distance() function in phyloseq was used. Principal coordinate analysis (PCoA) was then applied on the distance matrix using the ordinate() function in phyloseq. The resulting PCoA was visualized using the plot_ordination() function. Intra- and inter-sample dissimilarities were compared by analysing beta diversity, calculated using the fixed distance method with the “vegan” package. To test the significance of differences, pairwise differences were calculated by permutational multivariate analysis of variance (PERMANOVA) using the adonis2 function in the “vegan” package. Data visualization and statistical analyses The majority of statistical analysis and visualizations were performed using Phyloseq, microbiome and vegan R packages were used unless and otherwise specified [ 43 – 45 ]. R package “lefser” was used to infer differentially abundant bacteria [ 46 ]. Data and code availability All raw sequencing data i.e., fastq files were submitted to Short Read Archive (SRA), National Center for Biotechnology Information (NCBI) under the BioProject PRJNA942221 and BioSample accession numbers SRR23797948 – SRR23798204. All processed data i.e., quality control data, metadata and analysed full datasets are available on zenodo.org with DOI 10.5281/zenodo.7885592 ( https://doi.org/10.5281/zenodo.7885592 ). The full pipeline used for the analysis of sequencing data is available on the following link: https://github.com/dfmemicrobiota/infertile_couples . Results Study Population of infertile couples This study comprises samples from 65 infertile couples that were collected at the Luzern Cantonal Hospital, Switzerland between October 2018 and July 2020. We collected 257 samples from four genital tract niches, which consisted of 1) a vaginal swab and 2) follicular fluid from female partners, as well as 3) a semen sample and 4) a penile swab for male partners. Two penis swabs were not available. Infertility reasons were variable, with idiopathic infertility being the most frequently diagnosed condition (Table 1 ). Table 1 – Infertility reason Characteristics Count (percentage) Age (years) 41 11 (17%) Type of sterility Primary sterility 55 (86%) Secondary sterility 9 (14%) Reason for sterility Tubal factor 5 (8%) Male factor 16 (25%) Endometriosis I/II 4 (6%) Endometriosis III/IV 4 (6%) Anovulation 4 (6%) Idiopathic 31 (49%) Recurrent implantation failure 4 (6%) Previous miscarriage 12 (19%) Recurrent pregnancy loss 1 (2%) Bias-control refines genital microbiota analysis and reveals low biomass characteristics. To decipher species composition, we analysed bacterial communities by performing 16S rRNA gene amplicon sequencing and we report bacterial communities based on amplicon sequence variants (ASVs). We found a combined 5163 ASVs in all samples with 1285 ASVs in vaginal swabs, 1105 ASVs in follicular fluids, 2424 ASVs in penis swabs and 1508 ASVs in sperm samples. Controlling contamination in human microbiota especially in low biomass samples is a critical issue before reporting species structure and composition, due to the possible contamination introduced during DNA extraction and further processing prior to sequencing [ 47 ]. We sought to assess this issue by introducing several mitigation steps including absolute quantification of bacteria and inclusion of essential negative controls (n = 18). More specifically, the latter consisted of swabs opened in the room in which sampling was performed (n = 8) and washing buffer samples passed through the transvaginal follicle aspiration needle (n = 3). In addition, ultrapure water processed by the DNA extraction method (n = 5) and ultrapure water used as template for high-throughput sequencing (n = 2). First, we quantified sample-wise bacterial copy numbers by amplification of 16S rRNA gene by qPCR (Fig. 1 A). Vaginal samples had the highest bacterial load (range: 3.54x10 8 – 3x10 2 copies) followed by penis samples (range: 1.75x10 8 – 3x10 2 copies). Follicular fluid and sperm samples exhibited the lowest bacterial numbers. Indeed, this reveals that apart from the vagina all other genital tract niches were observed to be of low biomass. Next, we used the statistical method in the “decontam” package in R to infer probable contaminants based on the results obtained with the negative controls (Fig. 1 B). We investigated read retainment after decontaminations and removed amplicon sequence variants (ASVs) identified as contaminants from the analysis (Fig. 1 C) for each sample type. Finally, we normalized the data using quantified bacterial copy numbers reporting absolute numbers instead of relative abundance. Microbiota analysis reveals insights on the biogeography and gender-specific species composition in the human genital tract We investigated the species composition and site-specificity of the genital microbiota across gender. We also examined species prevalence and shared bacteria across different sampling sites in each gender. Members of the Lactobacillus genus were the most prevalent and abundant bacteria in female samples (both vagina and follicular fluid), with L. iners being the most prevalent species, which included two ASVs (ASV1 and ASV11 with 95.2% and 4.8% of all vaginal samples and 89.6% and 6.2% in follicular fluid) (Fig. 2 A and Supplementary Fig. 2). Indeed, L. iners ASV1 was the dominant ASV in 37 out of 112 female samples (33%) (Supplementary Fig. 2). As expected, L. crispatus was also highly prevalent in female samples, being the dominant species in 20 female samples (8 follicular fluid samples and 12 vaginal samples). Generally, despite a similar prevalence between the two types of samples, lactobacilli were less abundant in the follicular fluid compared to vaginal samples. Other highly prevalent species in female samples were Gardnerella vaginalis (found in 70% of vaginal samples and 31% follicular fluid samples) and Prevotella bivia (found in 62% of vaginal samples and 21% of follicular fluid samples). In male samples, L. iners (ASV1) was also the most prevalent bacterium, although present at low abundance (10 out of 92 male samples, 11%). Other prevalent bacteria included previously described genital bacteria like Gardnerella vaginalis and multiple Prevotella species. Skin-associated bacteria like Corynebacterium spp. and Staphylococcus epidermidis were also detected, implying their presence on the external male genitalia. In both female and male samples, a cluster Alphaproteobacteria ( Aquabacterium , Bradyrhizobium, Sphingomonas and Caulobacter spp. ) may represent putative contaminants that have not been eliminated by our filtering steps. Since samples were obtained from couples who were presumably sexually active, we wanted to investigate whether any of the genital niches included in the study shared bacterial species. We observed that only 3% (n = 137) of all ASVs were found in all four sample types were shared amongst these sites (Fig. 2 B), indicating the resilience of individual site-specific microbial consortia. The main shared genera were Lactobacillus spp. (n = 21), Prevotella spp. (n = 10 ), Staphylococcus spp. (n = 8) and Ezakiella spp. (n = 7). Further, to characterise the bacterial community structure and species diversity of the four genital niches, we performed alpha diversity analysis (observed number of ASVs, Shannon index and inverted Simpson index) (Fig. 2 C). As expected, vaginal samples showed the lowest bacterial diversity and evenness (lowest Shannon index and Inverted Simpson index, respectively), as most of them were dominated by Lactobacillus spp. Male genital samples showed the highest alpha diversity indices. While for penis samples this may represent a true species variability, given the global low bacterial biomass of this sample group, it may not be true for sperm samples. We did not observe any significant association of alpha diversity metrics and clinical data when analysing sample types separately or combined, except for follicular fluid samples and recurrent implantation failure (Supplementary table 1 ). As the lower genital tract may influence the bacterial composition of the upper genital tract, we calculated the Pearson correlation coefficient for intra-individual sample pairs (vagina vs follicular fluid and penis vs sperm – Fig. 2 D). A moderate positive but significant correlation was found between Shannon indexes of penis and sperm samples (R 2 = 0.51, P = 0.0031), while a weak positive but significant correlation was obtained for female samples (R 2 = 0.36, P = 0.014). To quantify the similarities between bacterial communities and discover differences between sample types, we performed beta diversity analysis using both unweighted and weighted Unifrac distances (Fig. 3 ). These results showed that the four sample types were indeed different from each other and these differences become more apparent when partially overlap when only phylogenetic distance compared (unweighted Unifrac distance) was compared (Fig. 3 A). Nevertheless, pairwise comparison of beta diversity showed that all sample pairs were significantly different. The same was true when both phylogeny and abundance was considered (weighted Unifrac) (Fig. 3 B). Here, most of the vaginal samples clustered together, given the high Lactobacillus spp. dominance. Analysis across individuals highlight potential interactions between genital microbiota To estimate whether there were any microbiota interactions between partners, we compared the beta diversity between samples from different individuals and samples from the same couple (Fig. 4 A). Despite generally high values, in several cases intra-couple dissimilarities were significantly lower when compared to intra-sample values (same sample type of different individuals). Semen and penis samples from the same individual were more similar compared to semen or penis samples from other individuals. Interestingly, the same was observed for follicular fluid and sperm samples, although this may be the caused by the presence of remaining contaminant ASVs present in these low bacterial biomass samples. On the other hand, intra-individual dissimilarities of vaginal samples were lower compared to other samples. To further explore inter-sample relationships from the same couple, Bray-Curtis dissimilarities were used to cluster samples based on their community composition (Supplementary Fig. 3). On several occasions sample pairs from the same patient clustered together, with variable dissimilarity values. This occurrence was higher in male samples (31% of patients in which both sperm and penis samples were present) compared to female samples (13%). Despite the possibility of contamination, this may suggest a possible influence of genital sites on each other. More specifically, given their higher bacterial load, vagina and penis may influence the bacterial composition of follicular fluid and sperm, respectively. A similar analysis was carried out in couples where inter-partner microbiota interaction was suspected (Fig. 4 B). The lowest dissimilarity values were observed for vaginal and penis samples from couple 0327 and between vaginal and sperm samples from couple 0317. In the first case, the dominant ASV in both sample types was Lactobacillus sp. ASV2. While lactobacilli are not frequently found in penis samples, this may suggest a transfer of microbiota from the vagina ( Lactobacillus sp. ASV2 is also found in the follicular sample) to the penis. In the second case, an influence of the male microbiota on the female colonisation could be suspected as the dominant ASV was Prevotella sp. ASV13 in both samples. This is further highlighted by the presence of two additional Prevotella spp. (ASV25 and ASV15) as the second and third dominant ASVs in both sample types. Specifically enriched genera Next, we sought to identify differentially abundant taxa (at genus level) specifically enriched between sites in female and male samples (Fig. 5 ). As expected, genus Lactobacillus was enriched in female samples, along with the genus Atopobium . In male samples, a number of genera were specifically enriched, the majority of which were previously associated with negative gynecological and obstetrical outcomes. These included Prevotella , Porphyromonas , Peptoniphilus , Finegoldia , Campylobacter , Mobiluncus among others. Discussion Despite the progress in the human reproduction medicine, an increasing number of infertility cases are still considered as idiopathic. Reproductive success may be influenced by multiple causes, including genetic, physiological and environmental causes. Since bacteria have an increasingly recognised influence on human homeostasis, it is tempting to explore the influence of the microbiota on human reproduction. Previous studies highlighted the impact of vaginal microbiota on pregnancy and sexually transmitted diseases [ 48 – 51 ]. Nevertheless, there is a knowledge gap in the field of genital microbiota compared to the plethora of studies about the influence of bacteria on other body sites. Most of the studies focus on the vagina, while the microbiota of the upper female and the male genital tract remain overlooked. Moreover, it is still unknown on how sexual activity may influence the bacterial colonisation of female genital tract and impact the fate of pregnancy. In this study, we explored the composition of genital microbiota in infertile couples, which included samples from both female and male partners. Given the limitations in the sampling of the upper genital, which may be highly invasive, we used samples that are routinely obtained during ART procedures, namely the follicular fluid (obtained during oocytes retrieval) and semen (used for the spermiogram analysis and in vitro fertilisation). Sampling of the lower genital tract included vaginal and penile swabs. Bacterial biomass was highly variable between sample types varying according to the sampling site on the body. As expected, Lactobacillus spp. were highly prevalent in vaginal samples, while the penis glans samples were moderately colonised by multiple bacterial species. On the other hand, follicular fluid and semen samples had generally low bacterial biomass. Here we also show unlike the vagina, other sampled body sites (follicular fluids, penis and semen) were primarily of low bacterial biomass. With increasing emphasis on proper reporting of microbial species from multiple human body sites, we applied stringent filtering procedure to limit the effect of possible contaminations on the results and use absolute bacterial copy numbers while reporting. As expected, Lactobacillus was the dominant genus among female samples, with a relative abundance above 90% in 45 out of 63 vaginal samples. Moreover, it was the dominant genus in 39 out of 48 follicular fluid samples included in the analysis. Previous studies suggested that vaginal microbiota may influence the colonisation of the upper genital tract. Nevertheless, we observed a related microbiota pattern in only 13% of women in which both vaginal and follicular fluid samples were present, while the remaining samples displayed differences in colonisation pattern and the dominant species. Thus, it appears that the upper female genital tract may have a distinctive low biomass microbiota, as previously suggested [ 15 , 52 , 53 ]. Further studies are required to characterise this specific microbiota, which is challenging due to the invasiveness of sampling and high risk of contamination, principally by the vaginal bacteria. Male samples were highly diverse compared to female samples. Despite this, semen and penis samples from the same individual, were more similar compared to samples of the same types from other men. This suggests that the penile microbiota and its relatively high bacterial load, may have an important influence on the bacterial composition of the ejaculate. Our results showed that male samples were specifically enriched with different bacterial genera previously associated with bacterial vaginosis ( Prevotella , Porphyromonas , Peptoniphilus , Finegoldia , Campylobacter , Mobiluncus genera among others) and therefore may represent a potential reservoir. One of the goals of this study was to evaluate possible interactions between female and male microbiota. Previous reports have shown that sexual intercourses may have an influence on the bacterial colonisation of the vagina, with a decrease in the relative abundance of Lactobacillus sp. [ 36 , 54 ]. Our results suggest a very limited impact of male microbiota on the female bacterial colonisation, even though we are missing the information about the sexual activity of the couples involved in the study. We highlight here a possible inter-partner interaction of microbiota in two cases. In the first one, penile microbiota composition was very similar to the partner’s vaginal microbiota, with Lactobacillus sp. ASV2 being the dominant ASV in both sample types, thus indicating a female to male bacterial transmission. In the second case, microbiota composition of the semen was highly similar to the partner’s vaginal microbiota. In this case, Prevotella sp. ASV13 was the dominant bacterium in both samples, thus suggesting a male to female transmission. Future analysis of the influence of sexual activity on microbial composition and variation of genital sites should ideally comprise samples obtained before and after sexual intercourse. Moreover, additional studies about the stability of the seminal microbiota based on longitudinal sampling of both penile swabs and semen should be performed. Declarations Ethics approval and Consent to participate This study was approved by the Ethics Committee Northwest and Central Switzerland (EKNZ - REPROLUKS003), according to the Swiss Federal Act on Research involving Human Beings. All patients gave their written consent for the utilisation of the samples. Consent for publication Not applicable. Availability of data and materials All raw sequencing files were submitted to the Short Read Archive (SRA), National Center for Biotechnology Information (NCBI) under the BioProject PRJNA942221 and BioSample accession numbers SRR23797948 – SRR23798204. All processed data, including quality control data, metadata and analysed full datasets are available on zenodo.org with DOI 10.5281/zenodo.7885592 (https://doi.org/10.5281/zenodo.7885592). The full pipeline used for the analysis of sequencing data is available on the following link: https://github.com/dfmemicrobiota/infertile_couples. Competing interests None to declare. Funding Not applicable. Authors' contributions Conceptualization of the study: DB, AP and MS. Sample collection and processing: AP, AV and MS. Data processing and analysis: PE, SD and MS. Manuscript draft preparation: DB, AP, SD and MS. Manuscript editing: DB, AP, AV, JMW, PE, SD and MS. All authors have reviewed the manuscript. Acknowledgements We are grateful to all the patients and staff that participated in the study. We thank the Lausanne Genomic Technologies Facility (GTF) of the University of Lausanne for performing high-throughput sequencing. References Valdes AM, Walter J, Segal E, Spector TD. Role of the gut microbiota in nutrition and health. BMJ [Internet]. British Medical Journal Publishing Group; 2018 [cited 2020 Aug 30];361. Available from: https://www.bmj.com/content/361/bmj.k2179 Gilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R. Current understanding of the human microbiome. Nature Medicine. Nature Publishing Group; 2018;24:392–400. Dominguez-Bello MG, Godoy-Vitorino F, Knight R, Blaser MJ. Role of the microbiome in human development. Gut. 2019;68:1108–14. Robertson RC, Manges AR, Finlay BB, Prendergast AJ. The Human Microbiome and Child Growth – First 1000 Days and Beyond. Trends in Microbiology. Elsevier; 2019;27:131–47. Gupta P, Singh MP, Goyal K. Diversity of Vaginal Microbiome in Pregnancy: Deciphering the Obscurity. Front Public Health. 2020;8:326. O’Callaghan JL, Turner R, Dekker Nitert M, Barrett HL, Clifton V, Pelzer ES. Re-assessing microbiomes in the low-biomass reproductive niche. BJOG. 2020;127:147–58. Heil BA, Paccamonti DL, Sones JL. Role for the mammalian female reproductive tract microbiome in pregnancy outcomes. Physiol Genomics. 2019;51:390–9. France M, Alizadeh M, Brown S, Ma B, Ravel J. Towards a deeper understanding of the vaginal microbiota. Nat Microbiol. Nature Publishing Group; 2022;7:367–78. Gajer P, Brotman RM, Bai G, Sakamoto J, Schutte UME, Zhong X, et al. Temporal Dynamics of the Human Vaginal Microbiota. Science Translational Medicine. 2012;4:132ra52-132ra52. Hickey RJ, Zhou X, Settles ML, Erb J, Malone K, Hansmann MA, et al. Vaginal microbiota of adolescent girls prior to the onset of menarche resemble those of reproductive-age women. mBio. 2015;6:e00097-15. Muhleisen AL, Herbst-Kralovetz MM. Menopause and the vaginal microbiome. Maturitas. 2016;91:42–50. Ravel J, Gajer P, Abdo Z, Schneider GM, Koenig SSK, McCulle SL, et al. Vaginal microbiome of reproductive-age women. Proc Natl Acad Sci USA. 2011;108 Suppl 1:4680–7. Mehta SD, Nannini DR, Otieno F, Green SJ, Agingu W, Landay A, et al. Host Genetic Factors Associated with Vaginal Microbiome Composition in Kenyan Women. mSystems. 2020;5:e00502-20. Peric A, Weiss J, Vulliemoz N, Baud D, Stojanov M. Bacterial Colonization of the Female Upper Genital Tract. International Journal of Molecular Sciences. Multidisciplinary Digital Publishing Institute; 2019;20:3405. 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. Nature Communications [Internet]. 2017 [cited 2017 Nov 28];8. Available from: http://www.nature.com/articles/s41467-017-00901-0 Moreno I, Franasiak JM. Endometrial microbiota—new player in town. Fertility and Sterility. 2017;108:32–9. Onderdonk AB, Delaney ML, Fichorova RN. The Human Microbiome during Bacterial Vaginosis. Clinical Microbiology Reviews. 2016;29:223–38. Schellenberg JJ, Patterson MH, Hill JE. Gardnerella vaginalis diversity and ecology in relation to vaginal symptoms. Research in Microbiology. 2017;168:837–44. Ravel J, Brotman RM, Gajer P, Ma B, Nandy M, Fadrosh DW, et al. Daily temporal dynamics of vaginal microbiota before, during and after episodes of bacterial vaginosis. Microbiome. 2013;1:29. Tang W, Mao J, Li KT, Walker JS, Chou R, Fu R, et al. Pregnancy and fertility-related adverse outcomes associated with Chlamydia trachomatis infection: a global systematic review and meta-analysis. Sex Transm Infect. BMJ Publishing Group Ltd; 2020;96:322–9. Jung H-S, Ehlers MM, Lombaard H, Redelinghuys MJ, Kock MM. Etiology of bacterial vaginosis and polymicrobial biofilm formation. Critical Reviews in Microbiology. 2017;43:651–67. van de Wijgert JHHM, Borgdorff H, Verhelst R, Crucitti T, Francis S, Verstraelen H, et al. The Vaginal Microbiota: What Have We Learned after a Decade of Molecular Characterization? Fredricks DN, editor. PLoS ONE. 2014;9:e105998. Smith SB, Ravel J. The vaginal microbiota, host defence and reproductive physiology. J Physiol (Lond). 2017;595:451–63. Amabebe E, Anumba DOC. The Vaginal Microenvironment: The Physiologic Role of Lactobacilli. Frontiers in Medicine [Internet]. 2018 [cited 2018 Oct 5];5. Available from: https://www.frontiersin.org/article/ 10.3389/fmed.2018.00181/full Satta A. Experimental Chlamydia trachomatis infection causes apoptosis in human sperm. Human Reproduction. 2005;21:134–7. Wølner-Hanssen P, Mårdh P-A. In vitro tests of the adherence of Chlamydia trachomatis to human spermatozoa. Fertility and Sterility. 1984;42:102–7. Nunez-Calonge R, Caballero P, Redondo C, Baquero F, Martinez-Ferrer M, Meseguer MA. Ureaplasma urealyticum reduces motility and induces membrane alterations in human spermatozoa. Human Reproduction. 1998;13:2756–61. Ahmadi MH, Mirsalehian A, Sadighi Gilani MA, Bahador A, Talebi M. Asymptomatic Infection With Mycoplasma hominis Negatively Affects Semen Parameters and Leads to Male Infertility as Confirmed by Improved Semen Parameters After Antibiotic Treatment. Urology. 2017;100:97–102. Baud D, Pattaroni C, Vulliemoz N, Castella V, Marsland BJ, Stojanov M. Sperm Microbiota and Its Impact on Semen Parameters. Frontiers in Microbiology [Internet]. 2019 [cited 2019 Mar 8];10. Available from: https://www.frontiersin.org/article/ 10.3389/fmicb.2019.00234/full Hou D, Zhou X, Zhong X, Settles ML, Herring J, Wang L, et al. Microbiota of the seminal fluid from healthy and infertile men. Fertility and Sterility. Elsevier; 2013;100:1261–1269.e3. Weng S-L, Chiu C-M, Lin F-M, Huang W-C, Liang C, Yang T, et al. Bacterial Communities in Semen from Men of Infertile Couples: Metagenomic Sequencing Reveals Relationships of Seminal Microbiota to Semen Quality. PLOS ONE. Public Library of Science; 2014;9:e110152. Štšepetova J, Baranova J, Simm J, Parm Ü, Rööp T, Sokmann S, et al. The complex microbiome from native semen to embryo culture environment in human in vitro fertilization procedure. Reprod Biol Endocrinol. 2020;18:3. Alfano M, Ferrarese R, Locatelli I, Ventimiglia E, Ippolito S, Gallina P, et al. Testicular microbiome in azoospermic men-first evidence of the impact of an altered microenvironment. Hum Reprod. 2018;33:1212–7. 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. 2019;34:1042–54. Koedooder R, Singer M, Schoenmakers S, Savelkoul PHM, Morré SA, de Jonge JD, et al. The ReceptIVFity cohort study protocol to validate the urogenital microbiome as predictor for IVF or IVF/ICSI outcome. Reprod Health. 2018;15:202. Mändar R, Punab M, Borovkova N, Lapp E, Kiiker R, Korrovits P, et al. Complementary seminovaginal microbiome in couples. Research in Microbiology. 2015;166:440–7. Borovkova N, Korrovits P, Ausmees K, Türk S, Jõers K, Punab M, et al. Influence of sexual intercourse on genital tract microbiota in infertile couples. Anaerobe. 2011;17:414–8. Rapin A, Pattaroni C, Marsland BJ, Harris NL. Microbiota Analysis Using an Illumina MiSeq Platform to Sequence 16S rRNA Genes. Curr Protoc Mouse Biol. 2017;7:100–29. Eren AM, Vineis JH, Morrison HG, Sogin ML. A Filtering Method to Generate High Quality Short Reads Using Illumina Paired-End Technology. PLOS ONE. Public Library of Science; 2013;8:e66643. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590-596. 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;6:226. McMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE. Public Library of Science; 2013;8:e61217. Lahti L, Shetty S. “microbiome R package.” 2022; Jari Oksanen, Gavin L. Simpson, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, Peter R. Minchin, R.B., O’Hara, Peter Solymos, M. Henry H. Stevens, Eduard Szoecs, Helene Wagner, Matt Barbour, Michael Bedward, Ben, Bolker, Daniel Borcard, Gustavo Carvalho, Michael Chirico, Miquel De Caceres, Sebastien Durand, Heloisa Beatriz, Antoniazi Evangelista, Rich FitzJohn, Michael Friendly, Brendan Furneaux, Geoffrey Hannigan, Mark O. Hill, Leo, Lahti, Dan McGlinn, Marie-Helene Ouellette, Eduardo Ribeiro Cunha, Tyler Smith, Adrian Stier, Cajo J.F. Ter Braak, and James Weedon. vegan: Community Ecology Package. 2022; Asya Khleborodova. lefser: R implementation of the LEfSE method for microbiome biomarker discovery. R package version 1.2.0. https://github.com/waldronlab/lefser . 2021; Molina NM, Plaza-Díaz J, Vilchez-Vargas R, Sola-Leyva A, Vargas E, Mendoza-Tesarik R, et al. Assessing the testicular sperm microbiome: a low-biomass site with abundant contamination. Reproductive BioMedicine Online. 2021;43:523–31. Tabatabaei N, Eren A, Barreiro L, Yotova V, Dumaine A, Allard C, et al. Vaginal microbiome in early pregnancy and subsequent risk of spontaneous preterm birth: a case-control study. BJOG: An International Journal of Obstetrics & Gynaecology. 2019;126:349–58. Fettweis JM, Serrano MG, Brooks JP, Edwards DJ, Girerd PH, Parikh HI, et al. The vaginal microbiome and preterm birth. Nature Medicine. 2019;25:1012–21. Nelson DB, Rockwell LC, Prioleau MD, Goetzl L. The role of the bacterial microbiota on reproductive and pregnancy health. Anaerobe. 2016;42:67–73. Green KA, Zarek SM, Catherino WH. Gynecologic health and disease in relation to the microbiome of the female reproductive tract. Fertility and Sterility. 2015;104:1351–7. Li F, Chen C, Wei W, Wang Z, Dai J, Hao L, et al. The metagenome of the female upper reproductive tract. GigaScience [Internet]. 2018 [cited 2019 Mar 14];7. Available from: https://academic.oup.com/gigascience/article/doi/ 10.1093/gigascience/giy107/5091799 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. American Journal of Obstetrics and Gynecology. 2015;212:611.e1-611.e9. Vodstrcil LA, Twin J, Garland SM, Fairley CK, Hocking JS, Law MG, et al. The influence of sexual activity on the vaginal microbiota and Gardnerella vaginalis clade diversity in young women. Fredricks DN, editor. PLOS ONE. 2017;12:e0171856. Additional Declarations No competing interests reported. Supplementary Files supplementarydata.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3063243","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":210330080,"identity":"fac8f98b-eddf-4cd3-88f7-ac127cdb7917","order_by":0,"name":"David Baud","email":"","orcid":"","institution":"Materno-fetal and Obstetrics Research Unit, Department Woman-Mother-Child, University Hospital of Lausanne","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Baud","suffix":""},{"id":210330081,"identity":"536059ef-e248-4653-8cf7-cb558b2b8a65","order_by":1,"name":"Adriana Peric","email":"","orcid":"","institution":"360° Fertility Center Zurich","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Adriana","middleName":"","lastName":"Peric","suffix":""},{"id":210330082,"identity":"bff582ba-f455-496a-9ffb-a97414a1ca68","order_by":2,"name":"Angela Vidal","email":"","orcid":"","institution":"Cantonal Hospital of Lucerne","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Angela","middleName":"","lastName":"Vidal","suffix":""},{"id":210330083,"identity":"d9b68866-98e7-41ae-bbdb-9138f0438f32","order_by":3,"name":"Jürgen M. Weiss","email":"","orcid":"","institution":"Fertility- and Endometriosiscenter, MVZ Kinderwunschteam Berlin GmbH","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jürgen","middleName":"M.","lastName":"Weiss","suffix":""},{"id":210330084,"identity":"28e2204d-dc8c-4df2-bbfe-4e9932ca3747","order_by":4,"name":"Philipp Engel","email":"","orcid":"","institution":"Department of Fundamental Microbiology, University of Lausanne","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Engel","suffix":""},{"id":210330085,"identity":"141f824e-9ef6-49d2-aed0-7bb68dafd464","order_by":5,"name":"Sudip Das","email":"","orcid":"","institution":"Lung Precision Medicine, Department for BioMedical Research (DBMR), University of Bern","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sudip","middleName":"","lastName":"Das","suffix":""},{"id":210330087,"identity":"97dc0b48-e561-4919-965f-09cdadb5c155","order_by":6,"name":"Milos Stojanov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACZuYGKOsAiLBhMABRjA3YVUO0QGQloFrSiNDCANcCBocJa9FtZ2x8+IOhro6f8fCzBx/bzsubS+QYMPzcgVuL2WHGZmMehsMSkg3HzA1ntt023Dkjx4Cx9wxeLW3SQH9IGBw4YCbNc+Z2gsGNtARmxjb8WiSBDgNqOf4NqOUccVokeBiYgVrOAG2pOADUknyAkBagXwwOS85sOFMmOaMi2XDDmccHDvbi03L+8MGHPyrq+Pkljm+T+GBgJ29wPLHxwU88WiAAFBcSBxD8AzjUoQH+BuLUjYJRMApGwcgDACC6Unxjf4xGAAAAAElFTkSuQmCC","orcid":"","institution":"Materno-fetal and Obstetrics Research Unit, Department Woman-Mother-Child, University Hospital of Lausanne","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Milos","middleName":"","lastName":"Stojanov","suffix":""}],"badges":[],"createdAt":"2023-06-14 13:29:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3063243/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3063243/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39092516,"identity":"f862e11d-10ed-43dc-9015-3e29f274ebc0","added_by":"auto","created_at":"2023-06-26 16:06:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":209744,"visible":true,"origin":"","legend":"\u003cp\u003eControlling for low abundance bacterial biomass samples. A. Quantification of bacterial load by qPCR. Partial amplification of the 16S rRNA gene was used to quantify the bacterial biomass in each of the four sample types. The dashed red line represents the limit of detection of 300 copies of the 16S rRNA gene. B. Prevalence of the taxa identified by “decontam” in the true samples and in negative controls. Scatter plot showing the prevalence of ASVs in samples (red) against negative controls (green - extraction and PCR water controls). C. All samples after the filtering steps. Boxplots depicting the reads per sample type, showing samples that were retained in the analysis (kept) and samples that were removed from the analysis (rem). The red dashed line represents the limit of 1000 reads under which samples were removed from the analysis. Fol: follicular fluid; pen: penis; spe: sperm; vag: vagina.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3063243/v1/dfce072bd488314612dad818.png"},{"id":39094529,"identity":"8f757747-ebab-46f7-ace5-d20dbe9b6ae6","added_by":"auto","created_at":"2023-06-26 16:22:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":671118,"visible":true,"origin":"","legend":"\u003cp\u003eA. Most prevalent ASVs in the female samples and male samples. Sequence alignment generated of 20 most prevalent ASVs using SINA and phylogenetic tree made using FastTree (left panel). Prevalence (red heatmap) and abundance (blue heatmap) of each ASV are shown as percentages. The closest hit (100% coverage and 99% sequence identity) is shown for each ASV on the right panel. B. Shared ASVs between sample types. Venn diagram of shared ASVs across the four sample types. C. Number of observed ASVs, Shannon and inverse Simpson indexes of the four sample types. D. Correlation of Shannon index between samples from same individuals. Boxplot of Shannon indexes of vaginal-follicular fluid (left panel) and sperm-penis (right panel) samples, in which samples from the same individual are connected by the black lines. On the right correlation plots between the samples, with the correlation coefficient and statistical significance. The regression line and the confidence interval are depicted in blue and grey, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3063243/v1/f44073d1200ee563dd9e94ac.png"},{"id":39093450,"identity":"5400675b-8b25-42b6-b1b9-2c688eca8188","added_by":"auto","created_at":"2023-06-26 16:14:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":361176,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis of (A) unweighted Unifrac and (B) weighted Unifrac distances. Lower panels show permutational multivariate analysis of variance between sample type pairs. Significant values (sig): p\u0026lt;0.01 *, p\u0026lt;0.05 \u003csup\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3063243/v1/0ce69f31c9ce78be1a498231.png"},{"id":39093451,"identity":"ff1785e1-e0b1-4cf7-95d5-71f66102ebd4","added_by":"auto","created_at":"2023-06-26 16:14:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":450893,"visible":true,"origin":"","legend":"\u003cp\u003eA. Comparison of intra- and inter-sample dissimilarities. The left panel depicts the schematic representation of sample comparisons (intra-individual, inter-individuals and intra-couple). On the right, Bray-Curtis dissimilarities calculated for each sample type. B. Hierarchical clustering dendrogram from the Bray-Curtis dissimilarity analysis in couples in which bacterial transmission was suspected. For each sample, the dominant ASV with species identification is displayed, along with the Shannon diversity index. Statistical analysis was performed by one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. p\u0026lt;0.01 *, p\u0026lt;0.001 **, p\u0026lt;0.0001 ***.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3063243/v1/792305e3f0f57d6241a303d3.png"},{"id":39092518,"identity":"ece5b5aa-ec3d-4013-852d-473e164c22de","added_by":"auto","created_at":"2023-06-26 16:06:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":223342,"visible":true,"origin":"","legend":"\u003cp\u003eSignature genera in female and male samples. Barplots depicting the effect size (log10 transformed LDA score) for specific genera. Relative abundance of depicted genera is displayed on the right panels for female and male samples.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3063243/v1/7743688c68d5a071c5e5b280.png"},{"id":56279841,"identity":"0e8d741d-ed70-45f1-b3d1-0ba2b8b1f8fc","added_by":"auto","created_at":"2024-05-10 20:47:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2520990,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3063243/v1/919de591-6b48-4774-bbbe-75f0b0f875ff.pdf"},{"id":39092521,"identity":"d9ed6e48-6b32-41b6-8727-3f85ceace9e2","added_by":"auto","created_at":"2023-06-26 16:06:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2026068,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-3063243/v1/9ecc7f90372e19bbd9065d02.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interaction of genital microbiota in infertile couples","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBacteria and other microorganisms colonise most of the ecological niches present in the human body [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. They are not only passive commensals but have a profound influence on the host\u0026rsquo;s homeostasis, playing a significant role at multiple levels, including protection against pathogens, maturation of the immune system, metabolic pathways, vitamin synthesis, among many others [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, it is not surprising that a disbalance or dysbiosis of the microbiota has been associated with several adverse outcomes.\u003c/p\u003e \u003cp\u003eThe impact of the microbiota on the genital tract is not an exception and an increasing number of studies exploring its role on pregnancy, infertility and adverse outcomes are being carried out [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Most studies focus on vaginal microbiota, which in a healthy state is dominated by members of the \u003cem\u003eLactobacillus\u003c/em\u003e genus [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nevertheless, the colonisation pattern may change over time [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. During the early stages of life, vaginal bacteria form low abundant and highly diverse communities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. During puberty, under the influence of sexual hormones, the vagina undergoes to important physiological changes, including the thickening of the epithelium and an increase in the production of glycogen, a condition that favours the proliferation of lactobacilli. Again, with hormonal changes during menopause, the proportion of lactobacilli diminish, leading to a more diverse and less abundant microbiota [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Variation of the composition of the vaginal microbiota may also occur at a shorter time scale and may be influenced by multiple factors. Studies on different human population suggest that the genetic background plays an important role in the stability of colonisation by lactobacilli, probably by the interplay of the host\u0026rsquo;s immune response [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Women of Caucasian and Asian origin tend to have a more stable, \u003cem\u003ei.e. Lactobacillus\u003c/em\u003e-dominated, microbiota compared to women of African and Hispanic origin [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, bacterial colonisation of other parts of the female genital tract has been characterised [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While previously considered sterile, the existence of specific microbial colonisation profiles has been described for the uterus, fallopian tubes and ovaries [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Several studies published in recent years, support the notion that a low-biomass microbiota is present in the uterine cavity and the rest of the upper genital tract, forming a continuum with the lower genital tract [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Compared to vaginal microbiota, there is an increased bacterial diversity, but there is still no clear evidence of the exact impact of the upper tract microbiota on health and reproductive outcome [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Increase in the diversity of vaginal bacteria (mostly anaerobic or facultative anaerobic bacteria) is associated with adverse gynecological and obstetrical outcomes. Bacteria that have been associated with a negative impact include, amongst others, \u003cem\u003eChlamydia trachomatis\u003c/em\u003e, \u003cem\u003eGardnerella vaginalis\u003c/em\u003e, \u003cem\u003ePrevotella spp.\u003c/em\u003e, \u003cem\u003eSneathia spp\u003c/em\u003e., \u003cem\u003eBacteroides spp\u003c/em\u003e., \u003cem\u003eMobiluncus spp.\u003c/em\u003e and \u003cem\u003eAtopobium vaginae\u003c/em\u003e [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This list is certainly not exhaustive as many other species might be associated with vaginal infections [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Women with a dysbiotic vaginal microbiota have an increased risk of developing both gynecological (cancer) and obstetrical (miscarriage, preterm birth) pathologies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, unlike other body sites, increased microbiota diversity is negatively associated with gynecological and obstetrical outcomes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, the pathophysiological mechanisms remain unknown as different combinations of genital bacteria can form the same pathology, as it is the case in bacterial vaginosis.\u003c/p\u003e \u003cp\u003eOther than vaginal colonisation, microbiota of the female upper genital has not been extensively investigated, compared to other body sites. Similarly, the male genital microbiota has been mostly neglected. Initial studies on the colonisation of the male genital tract focused on well-known genital pathogens (\u003cem\u003eChlamydia trachomatis\u003c/em\u003e, \u003cem\u003eUreaplasma spp.\u003c/em\u003e and \u003cem\u003eMycoplasma spp.\u003c/em\u003e, among others) or have relied on classical microbiological methods for culturing bacteria [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This led to the general view that semen is poorly colonised by bacteria, except in cases of ongoing infections where pathogens directly impair fertility. In recent years, bacterial communities colonising the male genital tract have been characterised using next generation sequencing [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It became clear that semen is not sterile and that bacteria are found not only in infertile men, but also in normozoospermic men (men with normal sperm parameters according to reference values set by the WHO, which include spermatozoa count, concentration, motility and morphology). Most of the studies have been performed on male partners of infertile couples, due to the availability of semen samples that were used for spermiogram analysis [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Despite multiple sequencing strategies, analysis pipelines and cohorts, similar community types were characterised, suggesting that a specific seminal microbiota might be necessary for optimal sperm function (or that an inadequate seminal microbiota may interfere negatively with sperm function). Presence of lactobacilli was often associated with good semen parameters, but their exact role remains to be elucidated. On the other hand, many bacterial genera found in semen were previously negatively associated with vaginal health and reports indicate that in some cases, sexual intercourse could lead to disturbed vaginal microbiota [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As an example, \u003cem\u003ePrevotella\u003c/em\u003e is one of the major genera encountered in semen and has been previously associated with bacterial vaginosis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Interestingly, an increased abundance of \u003cem\u003ePrevotella spp.\u003c/em\u003e was also negatively associated with semen parameters such as motility and morphology [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Other semen-associated genera that may negatively impact female genital tract include, amongst others, genera like \u003cem\u003eGardnerella\u003c/em\u003e, \u003cem\u003eAtopobium\u003c/em\u003e, \u003cem\u003eSneathia\u003c/em\u003e, \u003cem\u003eDialister\u003c/em\u003e and \u003cem\u003eFinegoldia\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the potential impact of seminal bacteria on human reproduction and dysbiosis of the female genital tract, this field of research remains understudied. In addition to investigating the composition of genital microbiota in infertile couples and its potential impact on infertility, our study also aimed to explore the potential interaction between male and female microbiota. By collecting samples from both male and female partners, we aimed to investigate, whether the microbiota of one partner could influence the composition of the other partner's microbiota. Due to the potential issues associated with analyses involving low biomass microbiota, like semen and follicular fluid samples, we implemented a series of negative controls and stringent \u003cem\u003ein silico\u003c/em\u003e elimination of possible contaminants to ensure the accuracy and reliability of our results.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and samples collection\u003c/h2\u003e \u003cp\u003eThis study comprises samples from couples diagnosed with infertility that were obtained from the Luzern Cantonal Hospital (Switzerland) between October 2018 and July 2020. All patients included in the study gave their written consent and utilization of the samples was approved by the Ethics Committee Northwest and Central Switzerland (EKNZ - REPROLUKS003), according to the Swiss Federal Act on Research involving Human Beings.\u003c/p\u003e \u003cp\u003eSamples were collected using eSwab (Copan Diagnostics, Italy) by direct sampling (vagina and penis glans) or by immersion in the biological fluid (follicular fluid and semen). Negative controls consisted of swabs that were opened in the same rooms in which the medical examination took part or processing of sterile water through the pump used to retrieve oocytes and follicular fluid (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Description of the sample types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample (abbreviation)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription and sampling\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVagina (vag)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVaginal swab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollicular fluid (fol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmersion of the swab in the follicular fluid obtained from oocyte retrieval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePenis (pen)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwab of the penis glans (self-sampling)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemen (spe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmersion of the swab in semen sample obtained for spermiogram analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl (ctrl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwab exposed to the environment in which sampling was performed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollicular fluid control (fol_ctrl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmersion of the swab in the sterile medium that has been passed in the aspiration device used for follicle retrieval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical control (tech_control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSterile water sample that has been processed for DNA extraction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater (water)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSterile water sample used as negative control for the amplification of the V1-V2 regions of the 16S rRNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity DNA Standard (comm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommercial Microbial Community DNA Standard\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eZymoBIOMICS Microbial Community DNA Standard (Zymo Research Corp., Irvine, CA, United States) was used to evaluate the potential variability of the sequencing results between different runs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and quantification of bacterial copy number\u003c/h2\u003e \u003cp\u003eTubes containing swabs were vortexed for 30 sec. A total of 500 \u0026micro;l of the tube medium was used for DNA extraction, which was performed with the QIAamp DNA mini kit (Qiagen AG, Basel, Switzerland). After an initial step of lysis using 0.3 \u0026micro;l Ready-Lyse Lysozyme (Epicentre, Madison, WI, USA) at 37\u0026deg;C with 500 rpm shaking for 60 min, extraction was done according to manufacturer\u0026rsquo;s specifications. Elution was done with 50 \u0026micro;l molecular biology grade H\u003csub\u003e2\u003c/sub\u003eO.\u003c/p\u003e \u003cp\u003eQuantification of bacterial 16S rRNA copy number was assessed by quantitative RT-PCR using 300 nM of both primers F-tot (5\u0026prime;-GCAGGCCTAACACATGCAAGTC-3\u0026prime;) and R-tot (5\u0026prime;-CTGCTGCCTCCCGTAGGAGT-3\u0026prime;) in the Rotor Gene 6000 thermocycler (Corbett Research, Sydney, Australia) with the iTaq Universal SYBR Green Supermix (Bio-Rad, Reinach, Switzerland).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBacterial 16S rRNA amplicon sequencing\u003c/h2\u003e \u003cp\u003eThe variable regions V1-V2 of the 16S rRNA gene were amplified using custom barcoded primers (F-27/R-338) containing Illumina sequencing adapters, as previously described[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. PCR was done with the Kapa HiFi PCR kit (KAPA Biosystems, Cape Town, South Africa) using 5 \u0026micro;l of extracted DNA as template and the following cycling conditions: 3 min initial denaturation at 95\u0026deg;C, followed by 30 cycles of 30 s at 98\u0026deg;C, 30 s at 56\u0026deg;C and 1 min 30 s at 72\u0026deg;C, with a final extension of 5 min at 72\u0026deg;C. Four libraries were prepared for a total of 282 samples, including the negative controls.\u003c/p\u003e \u003cp\u003ePrior sequencing, PCR product of each sample were evaluated by agarose gel electrophoresis (supplementary Fig.\u0026nbsp;1). Based on gel band intensities, samples were classified as \u0026ldquo;high\u0026rdquo; (H, strong band), \u0026ldquo;low\u0026rdquo; (L, visible band) and \u0026ldquo;null\u0026rdquo; (N, absence of a visible band). For each of the two libraries, 3 pools of samples corresponding gel band intensities were prepared (H, L and N). Each pool was purified and quantified, in order to maximise the number of reads for each sample. This step was necessary to avoid the overrepresentation of reads coming from the samples with high bacterial loads (\u003cem\u003ei.e.\u003c/em\u003e, most of the vaginal samples and a small proportion of penis samples).\u003c/p\u003e \u003cp\u003eIllumina sequencing was performed at the Lausanne Genomic Technologies Facility (GTF) of the Lausanne University using an Illumina MiSeq instrument in paired-end mode 2 x 250 nt (Illumina, San Diego, CA, United Sates).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBacterial community analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003ePre-processing and quality filtering\u003c/h2\u003e \u003cp\u003eDemultiplexing of the raw sequencing data was performed with the illumina-utils package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Read processing and phylogenetic sequencing analysis was performed with the DADA2 pipeline using default parameters [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] (Supplementary Data, GitHub pipeline). Read quality control, trimming, dereplication and filtering were also perform using the DADA2 pipeline. Samples with less than 1000 reads post processing were removed from the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, kept vs removed samples).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic assignment and phylogeny\u003c/h2\u003e \u003cp\u003eTaxonomy was assigned after alignment with SINA using the SILVA 16S rRNA database (SILVA NR v132) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], resulting in 16S rRNA gene amplicon sequencing variants (ASVs). Phylogenetic tree was constructed using FastTree version 2.1.10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDecontamination and normalization\u003c/h2\u003e \u003cp\u003eRelative abundance of ASVs were converted to absolute abundances by multiplying the 16S rDNA gene copy quantified by qPCR in each sample. Decontamination of sequencing data was performed with the \u0026ldquo;decontam\u0026rdquo; package in R [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The combined mode, in which both frequency and prevalence methods were used to identify putative contaminants, was used with the default settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiota diversity analyses\u003c/h2\u003e \u003cp\u003eAlpha diversity analyses were performed using the phyloseq function \u003cem\u003eplot_richness()\u003c/em\u003e, which uses the \u0026ldquo;vegan\u0026rdquo; package in R. Results were plotted and analysis of variance (ANOVA) was performed to calculate significant differences between sample types and other metadata variables. Paired sample wilcoxon test was used to investigate differences when categorized into sample type.\u003c/p\u003e \u003cp\u003eTo explore the beta diversity the unweighted and weighted UniFrac distances between samples using the \u003cem\u003edistance()\u003c/em\u003e function in phyloseq was used. Principal coordinate analysis (PCoA) was then applied on the distance matrix using the \u003cem\u003eordinate()\u003c/em\u003e function in phyloseq.\u0026nbsp;The resulting PCoA was visualized using the \u003cem\u003eplot_ordination()\u003c/em\u003e function. Intra- and inter-sample dissimilarities were compared by analysing beta diversity, calculated using the fixed distance method with the \u0026ldquo;vegan\u0026rdquo; package. To test the significance of differences, pairwise differences were calculated by permutational multivariate analysis of variance (PERMANOVA) using the \u003cem\u003eadonis2\u003c/em\u003e function in the \u0026ldquo;vegan\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData visualization and statistical analyses\u003c/h2\u003e \u003cp\u003eThe majority of statistical analysis and visualizations were performed using Phyloseq, microbiome and vegan R packages were used unless and otherwise specified [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. R package \u0026ldquo;lefser\u0026rdquo; was used to infer differentially abundant bacteria [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData and code availability\u003c/h2\u003e \u003cp\u003eAll raw sequencing data i.e., fastq files were submitted to Short Read Archive (SRA), National Center for Biotechnology Information (NCBI) under the BioProject PRJNA942221 and BioSample accession numbers SRR23797948 \u0026ndash; SRR23798204. All processed data i.e., quality control data, metadata and analysed full datasets are available on zenodo.org with DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.7885592\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.7885592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.7885592\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.7885592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The full pipeline used for the analysis of sequencing data is available on the following link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/dfmemicrobiota/infertile_couples\u003c/span\u003e\u003cspan address=\"https://github.com/dfmemicrobiota/infertile_couples\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population of infertile couples\u003c/h2\u003e \u003cp\u003eThis study comprises samples from 65 infertile couples that were collected at the Luzern Cantonal Hospital, Switzerland between October 2018 and July 2020. We collected 257 samples from four genital tract niches, which consisted of 1) a vaginal swab and 2) follicular fluid from female partners, as well as 3) a semen sample and 4) a penile swab for male partners. Two penis swabs were not available. Infertility reasons were variable, with idiopathic infertility being the most frequently diagnosed condition (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Infertility reason\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount (percentage)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25 Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of sterility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary sterility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary sterility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReason for sterility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubal factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis I/II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnovulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdiopathic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecurrent implantation failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious miscarriage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecurrent pregnancy loss\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBias-control refines genital microbiota analysis and reveals low biomass characteristics.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo decipher species composition, we analysed bacterial communities by performing 16S rRNA gene amplicon sequencing and we report bacterial communities based on amplicon sequence variants (ASVs). We found a combined 5163 ASVs in all samples with 1285 ASVs in vaginal swabs, 1105 ASVs in follicular fluids, 2424 ASVs in penis swabs and 1508 ASVs in sperm samples.\u003c/p\u003e \u003cp\u003eControlling contamination in human microbiota especially in low biomass samples is a critical issue before reporting species structure and composition, due to the possible contamination introduced during DNA extraction and further processing prior to sequencing [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. We sought to assess this issue by introducing several mitigation steps including absolute quantification of bacteria and inclusion of essential negative controls (n\u0026thinsp;=\u0026thinsp;18). More specifically, the latter consisted of swabs opened in the room in which sampling was performed (n\u0026thinsp;=\u0026thinsp;8) and washing buffer samples passed through the transvaginal follicle aspiration needle (n\u0026thinsp;=\u0026thinsp;3). In addition, ultrapure water processed by the DNA extraction method (n\u0026thinsp;=\u0026thinsp;5) and ultrapure water used as template for high-throughput sequencing (n\u0026thinsp;=\u0026thinsp;2). First, we quantified sample-wise bacterial copy numbers by amplification of 16S rRNA gene by qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVaginal samples had the highest bacterial load (range: 3.54x10\u003csup\u003e8\u003c/sup\u003e \u0026ndash; 3x10\u003csup\u003e2\u003c/sup\u003e copies) followed by penis samples (range: 1.75x10\u003csup\u003e8\u003c/sup\u003e \u0026ndash; 3x10\u003csup\u003e2\u003c/sup\u003e copies). Follicular fluid and sperm samples exhibited the lowest bacterial numbers. Indeed, this reveals that apart from the vagina all other genital tract niches were observed to be of low biomass. Next, we used the statistical method in the \u0026ldquo;decontam\u0026rdquo; package in R to infer probable contaminants based on the results obtained with the negative controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). We investigated read retainment after decontaminations and removed amplicon sequence variants (ASVs) identified as contaminants from the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) for each sample type. Finally, we normalized the data using quantified bacterial copy numbers reporting absolute numbers instead of relative abundance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrobiota analysis reveals insights on the biogeography and gender-specific species composition in the human genital tract\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe investigated the species composition and site-specificity of the genital microbiota across gender. We also examined species prevalence and shared bacteria across different sampling sites in each gender. Members of the \u003cem\u003eLactobacillus\u003c/em\u003e genus were the most prevalent and abundant bacteria in female samples (both vagina and follicular fluid), with \u003cem\u003eL. iners\u003c/em\u003e being the most prevalent species, which included two ASVs (ASV1 and ASV11 with 95.2% and 4.8% of all vaginal samples and 89.6% and 6.2% in follicular fluid) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Supplementary Fig.\u0026nbsp;2). Indeed, \u003cem\u003eL. iners\u003c/em\u003e ASV1 was the dominant ASV in 37 out of 112 female samples (33%) (Supplementary Fig.\u0026nbsp;2). As expected, \u003cem\u003eL. crispatus\u003c/em\u003e was also highly prevalent in female samples, being the dominant species in 20 female samples (8 follicular fluid samples and 12 vaginal samples). Generally, despite a similar prevalence between the two types of samples, lactobacilli were less abundant in the follicular fluid compared to vaginal samples. Other highly prevalent species in female samples were \u003cem\u003eGardnerella vaginalis\u003c/em\u003e (found in 70% of vaginal samples and 31% follicular fluid samples) and \u003cem\u003ePrevotella bivia\u003c/em\u003e (found in 62% of vaginal samples and 21% of follicular fluid samples). In male samples, \u003cem\u003eL. iners\u003c/em\u003e (ASV1) was also the most prevalent bacterium, although present at low abundance (10 out of 92 male samples, 11%).\u003c/p\u003e \u003cp\u003eOther prevalent bacteria included previously described genital bacteria like \u003cem\u003eGardnerella vaginalis\u003c/em\u003e and multiple \u003cem\u003ePrevotella\u003c/em\u003e species. Skin-associated bacteria like \u003cem\u003eCorynebacterium spp.\u003c/em\u003e and \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e were also detected, implying their presence on the external male genitalia. In both female and male samples, a cluster Alphaproteobacteria (\u003cem\u003eAquabacterium\u003c/em\u003e, \u003cem\u003eBradyrhizobium, Sphingomonas and Caulobacter spp.\u003c/em\u003e) may represent putative contaminants that have not been eliminated by our filtering steps.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince samples were obtained from couples who were presumably sexually active, we wanted to investigate whether any of the genital niches included in the study shared bacterial species. We observed that only 3% (n\u0026thinsp;=\u0026thinsp;137) of all ASVs were found in all four sample types were shared amongst these sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), indicating the resilience of individual site-specific microbial consortia. The main shared genera were \u003cem\u003eLactobacillus spp.\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;21), \u003cem\u003ePrevotella spp.\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;10\u003cem\u003e), Staphylococcus spp.\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;8) and \u003cem\u003eEzakiella spp.\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;7).\u003c/p\u003e \u003cp\u003eFurther, to characterise the bacterial community structure and species diversity of the four genital niches, we performed alpha diversity analysis (observed number of ASVs, Shannon index and inverted Simpson index) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). As expected, vaginal samples showed the lowest bacterial diversity and evenness (lowest Shannon index and Inverted Simpson index, respectively), as most of them were dominated by \u003cem\u003eLactobacillus spp.\u003c/em\u003e Male genital samples showed the highest alpha diversity indices. While for penis samples this may represent a true species variability, given the global low bacterial biomass of this sample group, it may not be true for sperm samples. We did not observe any significant association of alpha diversity metrics and clinical data when analysing sample types separately or combined, except for follicular fluid samples and recurrent implantation failure (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs the lower genital tract may influence the bacterial composition of the upper genital tract, we calculated the Pearson correlation coefficient for intra-individual sample pairs (vagina vs follicular fluid and penis vs sperm \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). A moderate positive but significant correlation was found between Shannon indexes of penis and sperm samples (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.51, P\u0026thinsp;=\u0026thinsp;0.0031), while a weak positive but significant correlation was obtained for female samples (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.36, P\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e \u003cp\u003eTo quantify the similarities between bacterial communities and discover differences between sample types, we performed beta diversity analysis using both unweighted and weighted Unifrac distances (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results showed that the four sample types were indeed different from each other and these differences become more apparent when partially overlap when only phylogenetic distance compared (unweighted Unifrac distance) was compared (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Nevertheless, pairwise comparison of beta diversity showed that all sample pairs were significantly different. The same was true when both phylogeny and abundance was considered (weighted Unifrac) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Here, most of the vaginal samples clustered together, given the high \u003cem\u003eLactobacillus spp.\u003c/em\u003e dominance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis across individuals highlight potential interactions between genital microbiota\u003c/h2\u003e \u003cp\u003eTo estimate whether there were any microbiota interactions between partners, we compared the beta diversity between samples from different individuals and samples from the same couple (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Despite generally high values, in several cases intra-couple dissimilarities were significantly lower when compared to intra-sample values (same sample type of different individuals). Semen and penis samples from the same individual were more similar compared to semen or penis samples from other individuals. Interestingly, the same was observed for follicular fluid and sperm samples, although this may be the caused by the presence of remaining contaminant ASVs present in these low bacterial biomass samples. On the other hand, intra-individual dissimilarities of vaginal samples were lower compared to other samples.\u003c/p\u003e \u003cp\u003eTo further explore inter-sample relationships from the same couple, Bray-Curtis dissimilarities were used to cluster samples based on their community composition (Supplementary Fig.\u0026nbsp;3). On several occasions sample pairs from the same patient clustered together, with variable dissimilarity values. This occurrence was higher in male samples (31% of patients in which both sperm and penis samples were present) compared to female samples (13%). Despite the possibility of contamination, this may suggest a possible influence of genital sites on each other. More specifically, given their higher bacterial load, vagina and penis may influence the bacterial composition of follicular fluid and sperm, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA similar analysis was carried out in couples where inter-partner microbiota interaction was suspected (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The lowest dissimilarity values were observed for vaginal and penis samples from couple 0327 and between vaginal and sperm samples from couple 0317. In the first case, the dominant ASV in both sample types was \u003cem\u003eLactobacillus sp.\u003c/em\u003e ASV2. While lactobacilli are not frequently found in penis samples, this may suggest a transfer of microbiota from the vagina (\u003cem\u003eLactobacillus sp.\u003c/em\u003e ASV2 is also found in the follicular sample) to the penis. In the second case, an influence of the male microbiota on the female colonisation could be suspected as the dominant ASV was \u003cem\u003ePrevotella sp.\u003c/em\u003e ASV13 in both samples. This is further highlighted by the presence of two additional \u003cem\u003ePrevotella spp.\u003c/em\u003e (ASV25 and ASV15) as the second and third dominant ASVs in both sample types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSpecifically enriched genera\u003c/h2\u003e \u003cp\u003eNext, we sought to identify differentially abundant taxa (at genus level) specifically enriched between sites in female and male samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs expected, genus \u003cem\u003eLactobacillus\u003c/em\u003e was enriched in female samples, along with the genus \u003cem\u003eAtopobium\u003c/em\u003e. In male samples, a number of genera were specifically enriched, the majority of which were previously associated with negative gynecological and obstetrical outcomes. These included \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003ePorphyromonas\u003c/em\u003e, \u003cem\u003ePeptoniphilus\u003c/em\u003e, \u003cem\u003eFinegoldia\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eMobiluncus\u003c/em\u003e among others.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite the progress in the human reproduction medicine, an increasing number of infertility cases are still considered as idiopathic. Reproductive success may be influenced by multiple causes, including genetic, physiological and environmental causes. Since bacteria have an increasingly recognised influence on human homeostasis, it is tempting to explore the influence of the microbiota on human reproduction. Previous studies highlighted the impact of vaginal microbiota on pregnancy and sexually transmitted diseases [\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Nevertheless, there is a knowledge gap in the field of genital microbiota compared to the plethora of studies about the influence of bacteria on other body sites. Most of the studies focus on the vagina, while the microbiota of the upper female and the male genital tract remain overlooked. Moreover, it is still unknown on how sexual activity may influence the bacterial colonisation of female genital tract and impact the fate of pregnancy.\u003c/p\u003e \u003cp\u003eIn this study, we explored the composition of genital microbiota in infertile couples, which included samples from both female and male partners. Given the limitations in the sampling of the upper genital, which may be highly invasive, we used samples that are routinely obtained during ART procedures, namely the follicular fluid (obtained during oocytes retrieval) and semen (used for the spermiogram analysis and \u003cem\u003ein vitro\u003c/em\u003e fertilisation). Sampling of the lower genital tract included vaginal and penile swabs.\u003c/p\u003e \u003cp\u003eBacterial biomass was highly variable between sample types varying according to the sampling site on the body. As expected, \u003cem\u003eLactobacillus spp.\u003c/em\u003e were highly prevalent in vaginal samples, while the penis glans samples were moderately colonised by multiple bacterial species. On the other hand, follicular fluid and semen samples had generally low bacterial biomass. Here we also show unlike the vagina, other sampled body sites (follicular fluids, penis and semen) were primarily of low bacterial biomass. With increasing emphasis on proper reporting of microbial species from multiple human body sites, we applied stringent filtering procedure to limit the effect of possible contaminations on the results and use absolute bacterial copy numbers while reporting.\u003c/p\u003e \u003cp\u003eAs expected, \u003cem\u003eLactobacillus\u003c/em\u003e was the dominant genus among female samples, with a relative abundance above 90% in 45 out of 63 vaginal samples. Moreover, it was the dominant genus in 39 out of 48 follicular fluid samples included in the analysis. Previous studies suggested that vaginal microbiota may influence the colonisation of the upper genital tract. Nevertheless, we observed a related microbiota pattern in only 13% of women in which both vaginal and follicular fluid samples were present, while the remaining samples displayed differences in colonisation pattern and the dominant species. Thus, it appears that the upper female genital tract may have a distinctive low biomass microbiota, as previously suggested [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Further studies are required to characterise this specific microbiota, which is challenging due to the invasiveness of sampling and high risk of contamination, principally by the vaginal bacteria.\u003c/p\u003e \u003cp\u003eMale samples were highly diverse compared to female samples. Despite this, semen and penis samples from the same individual, were more similar compared to samples of the same types from other men. This suggests that the penile microbiota and its relatively high bacterial load, may have an important influence on the bacterial composition of the ejaculate.\u003c/p\u003e \u003cp\u003eOur results showed that male samples were specifically enriched with different bacterial genera previously associated with bacterial vaginosis (\u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003ePorphyromonas\u003c/em\u003e, \u003cem\u003ePeptoniphilus\u003c/em\u003e, \u003cem\u003eFinegoldia\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eMobiluncus\u003c/em\u003e genera among others) and therefore may represent a potential reservoir.\u003c/p\u003e \u003cp\u003eOne of the goals of this study was to evaluate possible interactions between female and male microbiota. Previous reports have shown that sexual intercourses may have an influence on the bacterial colonisation of the vagina, with a decrease in the relative abundance of \u003cem\u003eLactobacillus sp.\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Our results suggest a very limited impact of male microbiota on the female bacterial colonisation, even though we are missing the information about the sexual activity of the couples involved in the study. We highlight here a possible inter-partner interaction of microbiota in two cases. In the first one, penile microbiota composition was very similar to the partner\u0026rsquo;s vaginal microbiota, with \u003cem\u003eLactobacillus sp.\u003c/em\u003e ASV2 being the dominant ASV in both sample types, thus indicating a female to male bacterial transmission. In the second case, microbiota composition of the semen was highly similar to the partner\u0026rsquo;s vaginal microbiota. In this case, \u003cem\u003ePrevotella sp.\u003c/em\u003e ASV13 was the dominant bacterium in both samples, thus suggesting a male to female transmission. Future analysis of the influence of sexual activity on microbial composition and variation of genital sites should ideally comprise samples obtained before and after sexual intercourse. Moreover, additional studies about the stability of the seminal microbiota based on longitudinal sampling of both penile swabs and semen should be performed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee Northwest and Central Switzerland (EKNZ - REPROLUKS003), according to the Swiss Federal Act on Research involving Human Beings. All patients gave their written consent for the utilisation of the samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw sequencing files were submitted to the Short Read Archive (SRA), National Center for Biotechnology Information (NCBI) under the BioProject PRJNA942221 and BioSample accession numbers SRR23797948 \u0026ndash; SRR23798204. All processed data, including quality control data, metadata and analysed full datasets are available on zenodo.org with DOI 10.5281/zenodo.7885592 (https://doi.org/10.5281/zenodo.7885592). The full pipeline used for the analysis of sequencing data is available on the following link: https://github.com/dfmemicrobiota/infertile_couples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Authors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization of the study: DB, AP and MS. Sample collection and processing: AP, AV and MS. Data processing and analysis: PE, SD and MS. Manuscript draft preparation: DB, AP, SD and MS. Manuscript editing: DB, AP, AV, JMW, PE, SD and MS. All authors have reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the patients and staff that participated in the study. We thank the Lausanne Genomic Technologies Facility (GTF) of the University of Lausanne for performing high-throughput sequencing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eValdes AM, Walter J, Segal E, Spector TD. Role of the gut microbiota in nutrition and health. BMJ [Internet]. British Medical Journal Publishing Group; 2018 [cited 2020 Aug 30];361. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bmj.com/content/361/bmj.k2179\u003c/span\u003e\u003cspan address=\"https://www.bmj.com/content/361/bmj.k2179\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R. Current understanding of the human microbiome. Nature Medicine. Nature Publishing Group; 2018;24:392\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDominguez-Bello MG, Godoy-Vitorino F, Knight R, Blaser MJ. Role of the microbiome in human development. Gut. 2019;68:1108\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobertson RC, Manges AR, Finlay BB, Prendergast AJ. The Human Microbiome and Child Growth \u0026ndash; First 1000 Days and Beyond. Trends in Microbiology. Elsevier; 2019;27:131\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta P, Singh MP, Goyal K. Diversity of Vaginal Microbiome in Pregnancy: Deciphering the Obscurity. Front Public Health. 2020;8:326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Callaghan JL, Turner R, Dekker Nitert M, Barrett HL, Clifton V, Pelzer ES. Re-assessing microbiomes in the low-biomass reproductive niche. BJOG. 2020;127:147\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeil BA, Paccamonti DL, Sones JL. Role for the mammalian female reproductive tract microbiome in pregnancy outcomes. Physiol Genomics. 2019;51:390\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrance M, Alizadeh M, Brown S, Ma B, Ravel J. Towards a deeper understanding of the vaginal microbiota. Nat Microbiol. Nature Publishing Group; 2022;7:367\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGajer P, Brotman RM, Bai G, Sakamoto J, Schutte UME, Zhong X, et al. Temporal Dynamics of the Human Vaginal Microbiota. Science Translational Medicine. 2012;4:132ra52-132ra52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHickey RJ, Zhou X, Settles ML, Erb J, Malone K, Hansmann MA, et al. Vaginal microbiota of adolescent girls prior to the onset of menarche resemble those of reproductive-age women. mBio. 2015;6:e00097-15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhleisen AL, Herbst-Kralovetz MM. Menopause and the vaginal microbiome. Maturitas. 2016;91:42\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavel J, Gajer P, Abdo Z, Schneider GM, Koenig SSK, McCulle SL, et al. Vaginal microbiome of reproductive-age women. Proc Natl Acad Sci USA. 2011;108 Suppl 1:4680\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehta SD, Nannini DR, Otieno F, Green SJ, Agingu W, Landay A, et al. Host Genetic Factors Associated with Vaginal Microbiome Composition in Kenyan Women. mSystems. 2020;5:e00502-20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeric A, Weiss J, Vulliemoz N, Baud D, Stojanov M. Bacterial Colonization of the Female Upper Genital Tract. International Journal of Molecular Sciences. Multidisciplinary Digital Publishing Institute; 2019;20:3405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen 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. Nature Communications [Internet]. 2017 [cited 2017 Nov 28];8. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nature.com/articles/s41467-017-00901-0\u003c/span\u003e\u003cspan address=\"http://www.nature.com/articles/s41467-017-00901-0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno I, Franasiak JM. Endometrial microbiota\u0026mdash;new player in town. Fertility and Sterility. 2017;108:32\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnderdonk AB, Delaney ML, Fichorova RN. The Human Microbiome during Bacterial Vaginosis. Clinical Microbiology Reviews. 2016;29:223\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchellenberg JJ, Patterson MH, Hill JE. Gardnerella vaginalis diversity and ecology in relation to vaginal symptoms. Research in Microbiology. 2017;168:837\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavel J, Brotman RM, Gajer P, Ma B, Nandy M, Fadrosh DW, et al. Daily temporal dynamics of vaginal microbiota before, during and after episodes of bacterial vaginosis. Microbiome. 2013;1:29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang W, Mao J, Li KT, Walker JS, Chou R, Fu R, et al. Pregnancy and fertility-related adverse outcomes associated with Chlamydia trachomatis infection: a global systematic review and meta-analysis. Sex Transm Infect. BMJ Publishing Group Ltd; 2020;96:322\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung H-S, Ehlers MM, Lombaard H, Redelinghuys MJ, Kock MM. Etiology of bacterial vaginosis and polymicrobial biofilm formation. Critical Reviews in Microbiology. 2017;43:651\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan de Wijgert JHHM, Borgdorff H, Verhelst R, Crucitti T, Francis S, Verstraelen H, et al. The Vaginal Microbiota: What Have We Learned after a Decade of Molecular Characterization? Fredricks DN, editor. PLoS ONE. 2014;9:e105998.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith SB, Ravel J. The vaginal microbiota, host defence and reproductive physiology. J Physiol (Lond). 2017;595:451\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmabebe E, Anumba DOC. The Vaginal Microenvironment: The Physiologic Role of Lactobacilli. Frontiers in Medicine [Internet]. 2018 [cited 2018 Oct 5];5. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/article/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/article/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2018.00181/full\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2018.00181/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatta A. Experimental Chlamydia trachomatis infection causes apoptosis in human sperm. Human Reproduction. 2005;21:134\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW\u0026oslash;lner-Hanssen P, M\u0026aring;rdh P-A. In vitro tests of the adherence of Chlamydia trachomatis to human spermatozoa. Fertility and Sterility. 1984;42:102\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNunez-Calonge R, Caballero P, Redondo C, Baquero F, Martinez-Ferrer M, Meseguer MA. Ureaplasma urealyticum reduces motility and induces membrane alterations in human spermatozoa. Human Reproduction. 1998;13:2756\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmadi MH, Mirsalehian A, Sadighi Gilani MA, Bahador A, Talebi M. Asymptomatic Infection With Mycoplasma hominis Negatively Affects Semen Parameters and Leads to Male Infertility as Confirmed by Improved Semen Parameters After Antibiotic Treatment. Urology. 2017;100:97\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaud D, Pattaroni C, Vulliemoz N, Castella V, Marsland BJ, Stojanov M. Sperm Microbiota and Its Impact on Semen Parameters. Frontiers in Microbiology [Internet]. 2019 [cited 2019 Mar 8];10. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/article/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/article/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2019.00234/full\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2019.00234/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou D, Zhou X, Zhong X, Settles ML, Herring J, Wang L, et al. Microbiota of the seminal fluid from healthy and infertile men. Fertility and Sterility. Elsevier; 2013;100:1261\u0026ndash;1269.e3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeng S-L, Chiu C-M, Lin F-M, Huang W-C, Liang C, Yang T, et al. Bacterial Communities in Semen from Men of Infertile Couples: Metagenomic Sequencing Reveals Relationships of Seminal Microbiota to Semen Quality. PLOS ONE. Public Library of Science; 2014;9:e110152.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠtšepetova J, Baranova J, Simm J, Parm \u0026Uuml;, R\u0026ouml;\u0026ouml;p T, Sokmann S, et al. The complex microbiome from native semen to embryo culture environment in human in vitro fertilization procedure. Reprod Biol Endocrinol. 2020;18:3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlfano M, Ferrarese R, Locatelli I, Ventimiglia E, Ippolito S, Gallina P, et al. Testicular microbiome in azoospermic men-first evidence of the impact of an altered microenvironment. Hum Reprod. 2018;33:1212\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoedooder R, Singer M, Schoenmakers S, Savelkoul PHM, Morr\u0026eacute; 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. 2019;34:1042\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoedooder R, Singer M, Schoenmakers S, Savelkoul PHM, Morr\u0026eacute; SA, de Jonge JD, et al. The ReceptIVFity cohort study protocol to validate the urogenital microbiome as predictor for IVF or IVF/ICSI outcome. Reprod Health. 2018;15:202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026auml;ndar R, Punab M, Borovkova N, Lapp E, Kiiker R, Korrovits P, et al. Complementary seminovaginal microbiome in couples. Research in Microbiology. 2015;166:440\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorovkova N, Korrovits P, Ausmees K, T\u0026uuml;rk S, J\u0026otilde;ers K, Punab M, et al. Influence of sexual intercourse on genital tract microbiota in infertile couples. Anaerobe. 2011;17:414\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRapin A, Pattaroni C, Marsland BJ, Harris NL. Microbiota Analysis Using an Illumina MiSeq Platform to Sequence 16S rRNA Genes. Curr Protoc Mouse Biol. 2017;7:100\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEren AM, Vineis JH, Morrison HG, Sogin ML. A Filtering Method to Generate High Quality Short Reads Using Illumina Paired-End Technology. PLOS ONE. Public Library of Science; 2013;8:e66643.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590-596.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis 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;6:226.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE. Public Library of Science; 2013;8:e61217.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLahti L, Shetty S. \u0026ldquo;microbiome R package.\u0026rdquo; 2022;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJari Oksanen, Gavin L. Simpson, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, Peter R. Minchin, R.B., O\u0026rsquo;Hara, Peter Solymos, M. Henry H. Stevens, Eduard Szoecs, Helene Wagner, Matt Barbour, Michael Bedward, Ben, Bolker, Daniel Borcard, Gustavo Carvalho, Michael Chirico, Miquel De Caceres, Sebastien Durand, Heloisa Beatriz, Antoniazi Evangelista, Rich FitzJohn, Michael Friendly, Brendan Furneaux, Geoffrey Hannigan, Mark O. Hill, Leo, Lahti, Dan McGlinn, Marie-Helene Ouellette, Eduardo Ribeiro Cunha, Tyler Smith, Adrian Stier, Cajo J.F. Ter Braak, and James Weedon. vegan: Community Ecology Package. 2022;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsya Khleborodova. lefser: R implementation of the LEfSE method for microbiome biomarker discovery. R package version 1.2.0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/waldronlab/lefser\u003c/span\u003e\u003cspan address=\"https://github.com/waldronlab/lefser\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2021;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolina NM, Plaza-D\u0026iacute;az J, Vilchez-Vargas R, Sola-Leyva A, Vargas E, Mendoza-Tesarik R, et al. Assessing the testicular sperm microbiome: a low-biomass site with abundant contamination. Reproductive BioMedicine Online. 2021;43:523\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabatabaei N, Eren A, Barreiro L, Yotova V, Dumaine A, Allard C, et al. Vaginal microbiome in early pregnancy and subsequent risk of spontaneous preterm birth: a case-control study. BJOG: An International Journal of Obstetrics \u0026amp; Gynaecology. 2019;126:349\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFettweis JM, Serrano MG, Brooks JP, Edwards DJ, Girerd PH, Parikh HI, et al. The vaginal microbiome and preterm birth. Nature Medicine. 2019;25:1012\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson DB, Rockwell LC, Prioleau MD, Goetzl L. The role of the bacterial microbiota on reproductive and pregnancy health. Anaerobe. 2016;42:67\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreen KA, Zarek SM, Catherino WH. Gynecologic health and disease in relation to the microbiome of the female reproductive tract. Fertility and Sterility. 2015;104:1351\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi F, Chen C, Wei W, Wang Z, Dai J, Hao L, et al. The metagenome of the female upper reproductive tract. GigaScience [Internet]. 2018 [cited 2019 Mar 14];7. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://academic.oup.com/gigascience/article/doi/\u003c/span\u003e\u003cspan address=\"https://academic.oup.com/gigascience/article/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/gigascience/giy107/5091799\u003c/span\u003e\u003cspan address=\"10.1093/gigascience/giy107/5091799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitchell 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. American Journal of Obstetrics and Gynecology. 2015;212:611.e1-611.e9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVodstrcil LA, Twin J, Garland SM, Fairley CK, Hocking JS, Law MG, et al. The influence of sexual activity on the vaginal microbiota and Gardnerella vaginalis clade diversity in young women. Fredricks DN, editor. PLOS ONE. 2017;12:e0171856.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3063243/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3063243/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBacteria colonise most of the human body and the genital tract is not an exception. While it has been known for decades that a vaginal microbiota exists, other genital sites have traditionally been viewed as sterile environments, with bacterial presence associated only with pathological conditions. However, recent studies identified specific patterns of bacterial colonisation in most genital sites. Shifts in the bacterial colonisation of the female genital tract have been linked to impairment of reproduction and adverse pregnancy outcomes, such as preterm birth. The goal of this project is to understand the association between the genital microbiota of couples seeking assisted procreation aid and the outcome of this treatment. Male and female partners were considered as a unit (\u0026ldquo;couple microbiota\u0026rdquo;) and the interaction between their microbiota will be evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe have characterised microbial samples coming from vaginal and penile swabs, as well as follicular fluid and semen, using next generation sequencing (16S rRNA profiling). The results revealed variability in bacterial biomass across different sample types, with \u003cem\u003eLactobacillus spp\u003c/em\u003e. dominating in vaginal and follicular fluid samples. Male samples exhibited higher diversity and harboured bacterial genera previously associated with negative obstetrical and gynecological outcomes. In addition, we found evidence of inter-partner microbiota interaction, indicating possible bacterial transmission between partners.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWith this project, we aimed to gain a better understanding of how the male genital microbiota could influence the lower (vagina) and upper (follicular fluid) female genital tracts. Our results suggest a very limited impact of male microbiota on the female bacterial colonisation, although the information about the sexual activity of the couples involved in the study was missing. Future research should focus on understanding the influence of sexual activity on microbial composition and stability in different genital sites, especially in the case of infertile couples.\u003c/p\u003e","manuscriptTitle":"Interaction of genital microbiota in infertile couples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-26 16:06:37","doi":"10.21203/rs.3.rs-3063243/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2da6764-f8ee-4321-9a42-032947c103ca","owner":[],"postedDate":"June 26th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-10T20:39:32+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-26 16:06:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3063243","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3063243","identity":"rs-3063243","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}