medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

1 Title:

2 Endometrial composition is associated with reproductive outcome in infertile

3 patients.

4 Authors:

5 Inmaculada Moreno1,2‡, Iolanda Garcia-Grau1,3‡, David Perez-Villaroya2, Marta Gonzalez-

6 Monfort1,2, Mustafa Bahçeci4, Marcelo J. Barrionuevo5, Sagiri Taguchi6, Elena Puente7,

7 Michael Dimattina8, Mei Wei Lim9, Georgina Meneghini10, Mira Aubuchon11, Mark

8 Leondires12, Alexandra Izquierdo13,¥, Martina Perez-Olgiati14, Alejandro Chavez15, Ken

9 Seethram16, Davide Bau2, Carlos Gomez2, Diana Valbuena2, Felipe Vilella1, Carlos

10 Simon1,2,3,17,18,*.

11 Affiliations:

12 1 Igenomix Foundation, INCLIVA Health Research Institute, Valencia, Spain.

13 2 Igenomix R&D, Valencia, Spain.

14 3 Department of Obstetrics & Gynecology, University of Valencia, Valencia, Spain.

15 4 Bahçeci Group, Istanbul, Turkey.

16 5 IVF Florida. Margate, Florida, USA.

17 6 Oak Clinic Sumiyoshi, Osaka, Osaka Prefecture, Japan.

18 7 Clinica Fertia, Fuengirola, Malaga. Spain.

19 8 Dominion Fertility, Arlington, Washington, USA.

20 9 Alpha IVF & Women’s Specialists Centre, Petaling Jaya, Selangor, Malaysia

21 10 Gestanza Medicina Reproductiva, Rosario, Santa Fe, Argentina.

22 11 Missouri Center for Reproductive Medicine, Chesterfield, Missouri, USA.

23 12 RMA Connecticut, Norwalk, Connecticut, USA.

24 13 ProcreaTec, Madrid, Spain.

25 14 Pregna Medicina Reproductiva, Buenos Aires, Argentina.

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

26 15 New Hope Fertility Center, Mexico City, Mexico.

27 16 Pacific Centre for Reproductive Medicine, Burnaby, British Columbia, Canada.

28 17 Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Harvard

29 Medical School, Boston, Massachusetts, USA.

30 18 Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, Texas,

31 USA.

32 ‡ Equal contribution

33 ¥ Present address: Médipôle Lyon-Villeurbanne, Villeurbanne, France.

34 *Correspondence: [email protected]

35

36 ABSTRACT

37 Background: Previous evidence indicates associations between the female reproductive tract 38 composition and reproductive outcome in infertile patients undergoing assisted 39 reproduction. We aimed to determine whether the endometrial microbiota composition is 40 associated with reproductive outcomes of live birth, biochemical , clinical 41 miscarriage, or no pregnancy.

42 Methods: Here we present a multicentre prospective observational study using 16S rRNA 43 gene sequencing to analyse endometrial fluid and biopsy samples before embryo transfer in a 44 cohort of 342 infertile patients asymptomatic for infection undergoing assisted reproductive 45 treatments.

46 Results: A dysbiotic endometrial microbiota profile composed of Atopobium, 47 Bifidobacterium, Chryseobacterium, Gardnerella, Haemophilus, Klebsiella, Neisseria, 48 Staphylococcus and Streptococcus was associated with unsuccessful outcomes. In contrast, 49 Lactobacillus was consistently enriched in patients with live birth outcomes.

50 Conclusions: Our findings indicate that endometrial microbiota composition before embryo 51 transfer is a useful biomarker to predict reproductive outcome, offering an opportunity to 52 further improve diagnosis and treatment strategies.

53 medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

54 INTRODUCTION

55 Humans have co-evolved as with microbial companions including , 56 , fungi, and (Simon et al. 2019). These microbes and their genetic 57 information (the microbiome) are now well-characterised; the Project 58 has revealed that approximately 9% of the total human microbiome is found in the female 59 reproductive tract (Peterson et al. 2009). Historically, all microbes were believed to inhabit 60 only the lower portion of the tract; the cervix was considered a perfect barrier between the 61 vagina and the upper genital tract that maintained sterility of the uterine cavity (Tissier 1900). 62 However, accumulating evidence demonstrates that this tract is an open system with a 63 microbiota continuum that gradually changes from the outer to the inner organs, with 64 decreasing bacterial abundance and increasing bacterial diversity from the vagina to the 65 ovaries (Walther-António et al. 2016; Miles, Hardy, and Merrell 2017; C. Chen et al. 2017; 66 Koedooder, Mackens, et al. 2019; Peric et al. 2019). There are reportedly 102–104 fewer 67 bacteria in the uterine cavity than in the vaginal microbiota (Mitchell et al. 2015; C. Chen et 68 al. 2017). Thus, the uterine cavity contains a low-abundance bacterial community, also 69 known as a low-biomass microbiota.

70 The presence of different in the female and male reproductive tracts might 71 influence reproductive function (Franasiak and Scott 2015; Koedooder, Mackens, et al. 2019; 72 Ravel, Moreno, and Simón 2020). Recent studies indicate that the chance of becoming 73 pregnant before the start of an in vitro fertilisation (IVF) treatment is stratified based on the 74 vaginal microbiota composition, as women with a low percentage of Lactobacillus in their 75 vaginal sample are less likely to have a successful embryo implantation (Koedooder, Singer, 76 et al. 2019). Analyses by 16S ribosomal RNA (16S rRNA) gene sequencing of endometrial 77 samples suggest that this microbiome impacts reproductive outcomes in infertile patients. 78 Having a Lactobacillus-dominated microbiota, defined as ≥90% or ≥80% Lactobacillus spp., 79 is associated with significantly increased implantation, pregnancy, ongoing pregnancy, and 80 live birth rates (Moreno et al. 2016; Kyono et al. 2019). Lately, 16S rRNA and whole 81 metagenomics sequencing have been used to investigate the endometrial microbiome (EM) in 82 cases of spontaneous clinical miscarriage (Moreno et al. 2020), recurrent miscarriage 83 (Garcia-Grau et al. 2019), and at 4 weeks gestation in a pregnancy resulting in a live birth 84 (Moreno et al. 2020). Interestingly, the EM during early successful pregnancy exhibited no 85 bacterial diversity and higher Lactobacillus abundance (Moreno et al. 2020). medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

86 In this prospective multicentre observational study, we investigated the EM composition in 87 342 infertile patients undergoing assisted reproductive technology (ART) in 13 different 88 centres on 3 continents by analysing endometrial fluid (EF) and endometrial biopsy (EB) 89 samples using 16S rRNA sequencing. The objective of the study was to determine whether 90 EM composition is associated with reproductive outcomes: live birth (LB), biochemical 91 pregnancy (BP), clinical miscarriage (CM), or no pregnancy (NP). Lactobacillus was 92 consistently more abundant than dysbiotic or pathogenic bacteria in women that achieved a 93 pregnancy with a LB. Lactobacillus spp. depletion and increased abundance of specific taxa 94 including Gardnerella, Haemophilus, Klebsiella, Neisseria, Staphylococcus, Streptococcus, 95 Atopobium, Bifidobacterium, and Chryseobacterium were associated with NP or CM. The 96 microbiota composition in EF did not fully reflect that in EB, although their association with 97 clinical outcome was consistent.

98 METHODS

99 Study design and sample size calculation

100 This was a multicentre prospective observational study analysing the EM of infertile patients 101 with maternal age ≤40 years undergoing IVF or ≤50 years undergoing ovum donation. The 102 EM was analysed in the same endometrial sample in which endometrial receptivity analysis 103 (ERA) was conducted in a cycle prior to embryo transfer of frozen blastocyst stage embryos 104 (day 5/6). Patients were all assessed in a hormone replacement therapy cycle after 120 h of 105 progesterone administration. The EF sample was aspirated before EB collection for EM 106 analysis by 16S rRNA sequencing. The EB was divided into two pieces for the study of 107 endometrial receptivity and EM analysis. Patients were treated following the standard 108 protocols in each clinic and the embryo was transferred on the day recommended by the ERA 109 test result using the same hormonal protocol as for the diagnostic cycle.

110 The main objectives of this prospective study were to validate our pilot study of the 111 endometrial microbiome and its impact on assisted reproduction in a larger number of 112 patients (Moreno et al. 2016), and to study the topologic effect of the endometrial 113 microbiome by comparing the microbial populations found in EF versus EB. To achieve 114 these objectives, we relied on the formula described for estimating the difference between 115 two proportions (hypothesis of contrasts in unilateral sense), with implantation, pregnancy 116 and ongoing pregnancy rates expected to be about 20% higher in patients with a normal medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

117 microbiome than in patients with an altered microbiome (95% CI and 80% of statistical 118 power). To detect this difference, we calculated that we would need to assess 146 patients. To 119 validate and compare the two endometrial sample types, we used the endometrial tissue as the 120 gold standard, and the most known and standardised test (ERA). We aimed to assess at least 121 100 non-receptive patients and 234 receptive patients to obtain a sensitivity and specificity of 122 90% (estimated using 10 cases in each marginal box of a 2x24 table). Because this last 123 calculation requires the greatest number of patients, we added 30% for possible losses, 124 bringing the total target number of patients to 434.

125 Study population — inclusion and exclusion criteria

126 From August 2017 to February 2019, 452 participants were recruited from 13 reproductive 127 clinics in Europe, America, and Asia. As this was a competitive study, each centre recruited 128 patients and sent their samples to Igenomix Foundation until the target sample size of the 129 study was reached. Inclusion criteria were patients undergoing IVF aged ≤40 years or patients 130 undertaking ovum donations aged ≤50 years; BMI of 18.5–30 kg/m2 (both inclusive); 131 negative serological tests for human immunodeficiency , Hepatitis B and C viruses, and 132 syphilis; regular menstrual formula (3–4 / 26–35 days); and >2 million sperm/mL. Exclusion 133 criteria included carriers of intrauterine devices or patients who took antibiotics in the last 3 134 months before sample collection (except mandatory prophylactic antibiotic treatment before 135 egg retrieval, which occurred one month before sample collection); presence of uncorrected 136 adnexal or uterine pathologies as uterine malformations; patients with severe or uncontrolled 137 bacterial, fungal or viral infections, or any illness or medical condition that risks the patient’s 138 safety.

139 Sample collection

140 EF and EB were collected following the previously described procedures (Vilella et al. 2013; 141 Simón et al. 2020). Briefly, with the patient in the lithotomy position, the vagina and cervix 142 were cleaned. After introduction of the disinfected speculum, a sterile and flexible catheter 143 (the same one used for embryo transfer) (Gynétics, Lommel, Belgium) was introduced into 144 the uterine cavity to aspirate EF, obtaining a volume between 20 and 80 μL. To prevent 145 contamination, any contact with vaginal walls was avoided and suction was stopped at the 146 entrance of the internal cervical os during catheter removal. Then, an EB sample (about 50– 147 70 mg tissue) was obtained with a cannula of Cornier (CCD Laboratories; Paris, France) by medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

148 scraping the . Once the EF and EB samples were obtained, they were transferred 149 to cryotubes containing 50 μL and 1.5 mL of RNAlater solution (Qiagen, Hilden, Germany), 150 respectively. Both samples were stored at 4°C for 4 hours, subsequently sent to Igenomix at 151 room temperature and stored at −80ºC until use.

152 Endometrial receptivity diagnosis

153 The ERA test (https://www.igenomix.com/genetic-solutions/era-endometrial-receptivity- 154 analysis/) was performed on the human RNA in each obtained EB sample. This test is used to 155 assess the personalised window of implantation of a given patient and determine the optimal 156 time frame for embryo transfer (Díaz-Gimeno et al. 2011; Ruiz-Alonso et al. 2013; Simón et 157 al. 2020).

158 DNA isolation

159 Total DNA was isolated from EF samples by performing a pre-digestion step at 37°C for 30 160 minutes with 25 ug/μL lysozyme, 0.12 U/μL lysostaphin, 0.4 U/μL mutanolysin and 1.8% 161 Triton X-100 to degrade the bacterial cell walls. DNA was then extracted with the QIAamp 162 DNA Blood Mini kit (Qiagen) following the manufacturer’s instructions. Finally, the DNA 163 was eluted with 35 μL of nuclease free water and quantified using a photometric technology 164 (Nanodrop, Waltham, MA, USA).

165 Total DNA was isolated from the EB samples by performing a pre-digestion step for 166 difficult-to-lyse bacteria. For this digestion, 25 mg of tissue was cut into small pieces and 167 treated with proteinase K at 56°C for 3 hours under agitation. Samples were then mixed with 168 ATL buffer and disrupted mechanically in a TissueLyser LT for 5 minutes at 50 Hz using 169 stainless-steel beads (all acquired from Qiagen). After these pretreatments, bacterial nucleic 170 acids were purified using the DNA tissue program V7-200-LC of the QIAsymphony (Qiagen) 171 following the manufacturer’s instructions. Finally, the DNA was eluted with 50 μL of 172 nuclease free water and quantified using MultiskanGO (Thermo Scientific, Waltham, MA, 173 USA).

174 16S ribosomal RNA gene sequencing

175 The EM profiles were obtained by next-generation sequencing using the Ion 16S 176 metagenomics kit (Thermo Fisher Scientific), which selectively amplifies 7 of the 9 medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

177 hypervariable regions of the bacterial gene encoding the 16S ribosomal subunit (V2-4-8 and 178 V3-6, 7-9). After amplification of the hypervariable regions with 10 μL of sample (per set of 179 primers) and 30 PCR cycles, the library was prepared starting from 50 ng of the pooled short 180 amplicons using the Ion Plus Fragment Library kit and Ion Xpress Barcode Adaptors 181 following the manufacturer’s instructions. The library concentration was adjusted using the 182 Ion Universal Library Quantitation Kit and QuantStudio 5 Real-Time PCR System. The 183 diluted individual libraries were then pooled for amplification by emersion PCR in the Ion 184 OneTouch 2 System (10 pM) or Ion Chef System (30 pM). Finally, libraries were sequenced 185 on the Ion S5 XL system using the Ion 530 Chip (all acquired from Thermo Fisher 186 Scientific).

187 To detect eventual contamination, each sequencing run included between two and four blank 188 samples as well as negative and positive PCR controls. The blank samples consisted of an 189 aliquot of the sample preservation buffer (RNA later; Qiagen), while positive and negative 190 PCR controls were pure microbial DNA from E. coli (3 ng) and nuclease-free water, 191 respectively (Thermo Fisher Scientific).

192 Sequencing-based microbiome analysis

193 The 16S rRNA sequencing results were analysed using the QIIME 2.0 package 194 (https://qiime2.org) and RDP classifier 2.2 for taxonomic assignment along with the 195 greengenes database version 13.8 (http://greengenes.second.genome.com), an update of 196 version 13.5 released to address missing genus and species names. For all processed samples, 197 the RDP classifier was run with the default minimum confidence estimate of 0.5 to record an 198 assignment (i.e., an operational taxonomic unit [OTU]). The results were generated using the 199 default parameters in the original QIIME 2 (Bolyen et al. 2019) and RDP (Wang et al. 2007) 200 methods. Identification and removal of contaminant sequences was conducted using the 201 decontam R Bioconductor package (Davis et al. 2018), using bacterial prevalence in blank 202 samples for statistical identification of contaminant taxa. Data were transformed to log-ratios 203 using the clr transformation (Aitchison 1986) making them symmetric and linearly related, 204 and thus potentially avoiding spurious correlations and sub-compositional incoherencies 205 (Calle 2019; Gloor et al. 2017; Pawlowsky-Glahn, Egozcue, and Tolosana-Delgado 2015).

206 Taking into account quality parameters such as the percentage of empty reads, the dispersion 207 index, and the ratio between filtered and mapped reads, samples were classified as detectable medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

208 or not detectable biomass based on the filtered versus mapped reads ratio threshold of 0.65 in 209 EF and 0.7 in EB samples. For further analysis, only the detectable samples (i.e. samples with 210 filtered versus mapped reads ratios greater than 0.65 and 0.7 in EF and EB, respectively) 211 were included. In each dataset of endometrial 16S rRNA profiles, the abundance of each taxa 212 was analysed, and low-abundance species were removed from further consideration. We 213 retained taxa that either (1) exhibited an abundance of at least 1% in 5% of the samples or (2) 214 exhibited an abundance of at least 0.1% in at least 15% of samples (Fettweis et al. 2019). 215 Taxa that failed to meet both criteria were removed. Genera not colonising humans or 216 associated with kitome contaminants were also removed from the analysis. Specifically, taxa 217 consistently reported to be contaminants in more than 5 of the 11 published references were 218 excluded (Tanner et al. 1998; Grahn et al. 2003; Barton et al. 2006; Laurence, Hatzis, and 219 Brash 2014; Lauder et al. 2016; Glassing et al. 2016; Stinson, Keelan, and Payne 2019; 220 Weyrich et al. 2019; Kyono et al. 2018; Hashimoto and Kyono 2019; Kitaya et al. 2019). 221 After applying these criteria, 15 and 19 OTUs were evaluated in EF and EB microbiota 222 respectively, with 12 OTUs shared between sample types (Supplementary Table 4).

223 Correlation network analysis

224 Pearson matrices for network analysis were generated using the Scipy Stats package (version 225 1.4.1) and visualised with Networkx (version 2.4), considering taxa with significant Pearson 226 correlation coefficient (Li et al. 2008). To account for both the correlation and significance, 227 each edge was assigned a weight w computed as: abs(corr) – pval, where abs(corr) was the 228 absolute correlation value between each pair of taxa and pval was the corresponding p-value. 229 Negative correlations were represented as red edges. Network analysis was performed on 230 each data type (i.e., EF and EB) and for each outcome (i.e., LB, NP, BP, and CM).

231 Taxa reference ranges

232 The log-ratio-transformed 16S rRNA profiles from LB samples were used to define the 233 reference range for each taxon. A confidence interval of 95% was calculated to establish 234 reference ranges for the Student’s t-distribution taxa distribution model (Almonacid et al. 235 2017). The Scipy Stats package (version 1.4.1) was used for calculations (Virtanen et al. 236 2020). medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

237 Taxa contributions to different outcomes were statistically inferred using a two-sided Mann- 238 Whitney U-test. The difference between samples outside the range and upper/lower bound 239 reference values was used to assess the role of the taxa in each outcome. Taxa were reported 240 when a significant difference was observed between upper and lower difference distributions. 241 The Scipy Stats package (version 1.4.1) (Virtanen et al. 2020) was used for statistical 242 methods and Plotly was used to visualise significant taxa distributions (Inc. 2015).

243 Bayesian inference for reproductive outcome differences

244 Differences between Lactobacillus and other reproductive tract taxa were modelled using

245 Bayesian inference following a normal distribution with mean *0/*( and dispersion

246 *0/*( for each reproductive outcome,

247 $% *0/*( ,*0/*( .

248 Priors selected to estimate parameters were normal distribution for *0/*( centred on the 249 mean difference between Lactobacillus and other taxa in the dataset and a dispersion of 10

250 (*/0*( ˉ,10) and, for *0/*( , vague continuous distribution from 0 to 10

251 (*0/*( 0,10. Markov chain Monte Carlo sampling for posterior distribution 252 parameter inference and predictive probability analysis were implemented using the PyMC3 253 Python library (Salvatier, Wiecki, and Fonnesbeck 2016).

254 Statistical analysis

255 All data were analysed on a per-protocol basis. One-way analysis of variance was used to

256 compare non-categorical variables among groups by reproductive outcomes. Mean

257 differences and standard deviation or median and interquartile ranges were used when the

258 variables were not homogeneous, as well as the mean differences with 95% CI values.

259 Categorical variables were described by counts (n) and percentages (%), and the Chi-square

260 test and two-sided Fisher's exact test were used to compare groups by reproductive outcomes

261 with respect to percentages. Multiple-comparison post-hoc correction (Bonferroni) was

262 applied for all pairwise comparisons. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

263 Multinomial logistic regression analysis was conducted to control possible confounding

264 factors, effect modifiers, to demonstrate the homogeneity of the key variables and the

265 absence of bias towards our final endpoint. A multinomial endpoint was included as a

266 dependent variable (LB/NP/BP/CM) and predictors shown in Supplementary Table 1 and 2. P

267 < 0.05 was considered to be statistically significant. All analyses were conducted using SPSS

268 25 software (IBM, MD, USA) and R version 3.6.3 (The CRAN project).

269 RESULTS

270 Patient cohort, characteristics, and outcomes

271 A total of 452 patients with infertility undergoing IVF were assessed for eligibility in 13 272 reproductive clinics in Europe, America, and Asia between August 2017 and February 2019 273 (Figure 1). Forty-four patients were excluded from the study because they did not meet the 274 inclusion criteria (n=42) or declined to participate (n=2). The remaining 408 women were 275 recruited and their EB and EF microbiota composition was assessed by 16S rRNA 276 sequencing. However, 66 patients were lost to follow-up. Of the 342 remaining patients, 198 277 (57.9%) became pregnant [141 (41.2%) had a LB, 27 (7.9%) had a BP and 28 (8.2%) a CM], 278 while 144 (42.1%) did not become pregnant (Supplementary Table 1). Moreover, 2 patients 279 experienced an ectopic pregnancy, but their results were not considered for further 280 comparisons due to the small sample size (Figure 1). Analysed patients had a mean age of 36 281 years (range 21–49), and a mean body mass index (BMI) of 23.3 (range 18.5–30.0). The 282 ethnic distribution was Caucasian (57.3%), East Asian (14.0%), Hispanic (11.4%), and others 283 (17.3%). The indications for IVF were advanced maternal age, male factor infertility, 284 unexplained infertility, and ovarian pathology. The assessed clinical and embryological 285 variables displayed homogeneity and no bias toward the clinical outcome categories was 286 observed (Supplementary Table 2).

287 Endometrial microbiota composition in EF and EB 288 The EM was profiled in EF from 336 patients and EB for 296 patients, with paired EF–EB 289 results in 290 (84.8%) participants. The mean total sequencing reads per sample was 302,299 290 (range, 110,050–394,659) in EF samples, and 335,659 (range, 237,889–430,675) in EB medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

291 samples, with an average of 89,883 (range, 27,960–137,956) and 103,539 filtered reads 292 (range, 61,650–162,653), respectively (Supplementary Table 3).

293 Because the endometrium presents a low-abundance microbiota, stringent analysis was 294 performed to ensure that contaminating reads did not interfere with downstream analysis. 295 Samples were classified as detectable and not-detectable by comparing them to blank samples 296 included in each run and assessing certain quality parameters (see criteria in Materials and 297 Methods). Despite starting with equivalent amounts of extracted DNA, detectable samples 298 showed a different clustering behaviour as compared with not-detectable/low-biomass 299 samples (with a higher 16S amplicon concentration), which clustered together with blank 300 controls (Figure 2). After applying these criteria, 208 EF samples and 190 EB samples were 301 classified as detectable and included in the analysis (Figure 2).

302 Lactobacillus was the major genus in both EF and EB samples. Bacterial genera such as 303 Anaerococcus, Atopobium, Bifidobacterium, Corynebacterium, Gardnerella, Haemophilus, 304 Microbacterium, , Propionibacterium, Staphylococcus, and Streptococcus were 305 also commonly identified in both sample types (Supplementary Figure 1). Streptomyces, 306 Clostridium, and Chryseobacterium were detected in EF but not EB, whereas Cupriavidus, 307 , Klebsiella, , Finegoldia, Micrococcus, and Tepidimonas were detected 308 in EB but not EF (Supplementary Figure 1).

309 The co-occurring EM bacterial networks showed several differences between the sample 310 types: (i) the EF microbiota had two linked communities, while the EB microbiota had four 311 linked communities and two isolated nodes; (ii) the EF microbiota network was more 312 strongly connected than the EB community; and (iii) Lactobacillus had positive and negative 313 connected neighbours in the EF microbiota, but only negative relations in the EB microbiota 314 (Figure 3). In the EB microbiota, Lactobacillus was negatively correlated with pathogenic 315 bacteria Gardnerella, Bifidobacterium, and Atopobium, whereas in EF, Lactobacillus was 316 negatively correlated with Gardnerella, Bifidobacterium, Atopobium, Staphylococcus, 317 Streptococcus, and Chryseobacterium, and positively correlated to commensal bacteria 318 (Clostridium and Streptomyces).

319 Endometrial microbiota composition and reproductive outcome medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

320 To analyse the association between EM composition in EF and EB and reproductive 321 outcome, we built microbiota networks for each reproductive outcome. We found that the LB 322 category was denser and had a higher node degree distribution than co-occurrence networks 323 of unsuccessful reproductive outcomes. Additionally, we found potential interactions that 324 only occurred in patients with LB, reflecting the relevance of these relationships to successful 325 pregnancy and how their disruption may lead to ecosystem instability. We also noted that in 326 the EF microbiota of patients who had a LB, Lactobacillus was negatively related to 327 dysbiotic bacteria such as Chryseobacterium, Staphylococcus and Haemophilus, and 328 positively correlated to Streptomyces, which in turn was part of a dense community mainly 329 composed of commensal bacteria such as Corynebacterium, Microbacterium, 330 Propionibacterium, and Clostridium. In patients with NP, we identified a similar behaviour, 331 with Lactobacillus negatively correlated with Gardnerella, Bifidobacterium, Atopobium, 332 Staphylococcus, Streptococcus, and Chryseobacterium, and positively related to 333 Streptomyces. Interestingly, in the group of patients with BP and CM, these interactions 334 disappeared, and the resulting networks were disconnected and formed sparse communities 335 (Figure 4A). Finally, the EB microbiota networks were more dispersed than the EF ones, with 336 fewer interactions between Lactobacillus and other taxa. Thus, in this case, the eventual 337 beneficial/deleterious connections among taxa were less evident (Figure 4B).

338 To avoid potential bias when comparing samples analysed in different runs, bacterial profiles 339 were transformed into centred log ratio (clr) data, and the bacterial communities were 340 analysed according to the difference between Lactobacillus and other reproductive tract taxa 341 using z-score-normalised values. Using these conditions, higher abundance of Lactobacillus 342 was observed in both EF and EB in patients with LB compared to patients with negative 343 reproductive outcomes (Figure 5A). Taxa with a higher average abundance in unsuccessful 344 outcomes than in LB included Streptococcus, Chryseobacterium, Corynebacterium, 345 Haemophilus, Bifidobacterium, Staphylococcus, Atopobium, Gardnerella, and 346 Propionibacterium in the EF microbiota and Gardnerella, Klebsiella, Atopobium, Finegoldia, 347 Escherichia, Propionibacterium, Haemophilus, Anaerococcus, and Bacillus in the EB 348 microbiota (Supplementary Figure 2). Predictive probability analysis using a Bayesian 349 inference model showed a different highest posterior density (HPD) interval of the difference 350 “Lactobacillus – other taxa” for each ART outcome: LB (-0.12–1.51), NP (-1.05–0.65), BP (- 351 3.45–0.81), and CM (-2.81–0.55). Patients with a LB were more likely to have a higher 352 abundance of Lactobacillus (Figure 5B). This increased probability of higher Lactobacillus medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

353 abundance in LB was especially distinct in EF samples, where the HPD for LB samples 354 showed less overlap with unsuccessful outcome intervals.

355 Finally, we compared the EM bacterial profiles in patients who achieved a successful 356 pregnancy with LB versus those with unsuccessful outcomes (BP, CM and NP). Our 357 hypothesis was that the microbiota composition in patients with LB is the physiological 358 scenario and does not interfere with functional reproductive potential. Therefore, we 359 evaluated the distance between the abundance of each bacterial taxon and reproductive 360 outcome in the upper and lower 95% confidence intervals established for patients with LB 361 (Figure 6). In patients with NP, the EF taxa with significantly higher abundance exceeding 362 the established upper confidence interval were Atopobium, Bifidobacterium, 363 Chryseobacterium, Gardnerella, and Streptococcus, and in those with CM, Haemophilus and 364 Staphylococcus exceeded the physiological levels. By contrast, taxa with a significantly 365 higher distance to the lower established confidence interval were Lactobacillus and 366 Microbacterium in NP patients, and Lactobacillus in patients with CM (Figure 7A). In the EB 367 microbiota of NP patients, Bifidobacterium, Gardnerella and Klebsiella were significantly 368 more abundant, and the abundance of Cupriavidus, Finegoldia, Lactobacillus, and 369 Tepidomonas was significantly below the established confidence interval (Figure 7B). The 370 remaining comparisons did not reach statistical significance, possibly due to the small 371 number of patients with BP and CM.

372 Endometrial microbiota composition in chronic endometritis and reproductive outcome

373 We also evaluated the abundance of the main pathogenic bacteria reported to cause chronic 374 endometritis (CE): Enterococcus, Escherichia, Klebsiella, Streptococcus, Staphylococcus, 375 Gardnerella, , Ureaplasma, Chlamydia, and Neisseria. These bacteria are 376 considered to be a potential cause of infertility as well as obstetric and neonatal complications 377 (Kitaya et al. 2016; Kitaya et al. 2018). We compared the abundance of these bacteria with 378 the confidence interval generated for infertile patients that achieved a LB.

379 Of the CE pathogens, Gardnerella, Klebsiella, and Streptococcus were significantly 380 increased in the EF microbiota of NP patients, whereas Enterococcus was increased in 381 patients that experienced BPs, and Klebsiella and Staphylococcus were increased in CM 382 (Figure 8A). In the EB microbiota, Gardnerella, Neisseria, and Klebsiella were significantly 383 enriched in women with NP compared to those that achieved LB, while Enterococcus medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

384 abundance was below the confidence interval (Figure 8B). In the remaining unsuccessful 385 reproductive categories (BP and CM), no significant taxa were detected. Interestingly, 386 Gardnerella and Klebsiella were the only common pathogens significantly enriched in both 387 EF and EB from patients with NP.

388 Endometrial microbiota composition fingerprinting is associated with reproductive 389 outcome

390 In summary, the pathogenic profile associated with reproductive failure in our cohort of 391 infertile patients consisted of Atopobium, Bifidobacterium, Chryseobacterium, Gardnerella, 392 Haemophilus, Klebsiella, Neisseria, Staphylococcus, and Streptococcus. In contrast, 393 Lactobacillus was consistently enriched in the EM (both in EF and EB) from patients that 394 achieved LB. Therefore, patients with a higher abundance of lactobacilli are more likely to 395 achieve reproductive success. Also, some commensal bacteria such as Cupriavidus, 396 Finegoldia, Microbacterium and Tepidimonas were positively associated with LB.

397 DISCUSSION

398 Some diseases or organ malfunctions are marked by the presence of potentially pathogenic 399 microbes, whereas others are characterised by depletion of health-associated bacteria. The 400 vaginal microbiota composition is associated with obstetrical outcome (Fettweis et al. 2019; 401 Koedooder, Singer, et al. 2019). However, no consensus has been reached on the profile of 402 endometrial bacterial pathogens and the mechanisms by which they could interfere with 403 embryo implantation (Benner et al. 2018; Baker, Chase, and Herbst-Kralovetz 2018).

404 In this study, we investigated the relationship between EM composition and the reproductive 405 fate of a cohort of 342 ethnically diverse infertile patients from three different continents. Our 406 results suggest that the composition of the EM at the time of conception is associated with the 407 reproductive outcome. The co-occurrence bacterial association plots built separately for each 408 ART outcome showed that the LB networks were denser and had a higher node degree 409 distribution than the networks of failed outcomes. Moreover, Lactobacillus was generally 410 negatively correlated to pathogenic bacteria and positively correlated to commensal bacteria, 411 which may be important for stability in the ecosystem. Lactobacillus depletion and the 412 presence of specific pathogenic bacteria such as Atopobium, Bifidobacterium, 413 Chryseobacterium, Gardnerella, Streptococcus, and Klebsiella in EF, and/or medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

414 Bifidobacterium, Gardnerella, Klebsiella, and Neisseria in EB were associated with 415 unsuccessful reproductive outcomes.

416 Atopobium vaginae and Gardnerella vaginalis are the major bacterial vaginosis-associated 417 pathogens; they stimulate an innate immune response from vaginal epithelial cells (Libby et 418 al. 2008). However, the deleterious impact of these pathogens is not restricted to the vagina. 419 Women with bacterial vaginosis subjected to laparoscopic salpingectomy or curettage present 420 significantly increased risk of developing endometrial polymicrobial with G. 421 vaginalis and other bacteria (Swidsinski et al. 2013). Streptococcus agalactiae, Klebsiella 422 pneumoniae, Enterococcus faecalis, Neisseria gonorrhoeae, and in some studies G. 423 vaginalis, are the major pathogens of CE (Kitaya et al. 2018; Cicinelli et al. 2008; Moreno et 424 al. 2018). This condition impairs reproductive outcomes in natural conception and after ART, 425 further contributing to obstetric and neonatal complications (Kitaya et al. 2016). A case series 426 study of three women with recurrent implantation failure and CE showed the elimination of 427 CE-associated pathogens and subsequent restoration of fertility after intrauterine antibiotic 428 administration (Sfakianoudis et al. 2018). Moreover, bacteria such as S. agalactiae are known 429 to be one of the leading causes of neonatal infections by vertical transmission (Patras and 430 Nizet 2018). Finally, in the absence of Lactobacillus, Bifidobacterium are hypothesised to be 431 able to maintain healthy vaginal balance by the production of lactic acid (Freitas and Hill 432 2017; Kyono et al. 2019). However, our results show a negative association of 433 Bifidobacterium with LB, in agreement with other reports demonstrating Bifidobacterium 434 species to be pathogenic in various infectious conditions (Bhaskar, Sistla, and Kumaravel 435 2017; Pathak, Trilligan, and Rapose 2014; Y. Chen et al. 2019). Hence, the role of members 436 of this genus should be further investigated.

437 We observed a close relationship between EF and EB microbiota, although there were some 438 differences between the sample types. Specifically, 12 of the 15 most abundant taxa detected 439 in EF overlapped with the taxa identified in the EB samples, but three taxonomies were 440 unique for EF (Streptomyces, Clostridium, and Chryseobacterium) and seven were detected 441 only in EB (Cupriavidus, Escherichia, Klebsiella, Bacillus, Finegoldia, Micrococcus, and 442 Tepidimonas). A possible explanation for the difference in bacterial taxa between the sample 443 sources is that bacteria present on the surface of the luminal epithelium may be different than 444 those found deeper, close to the glandular epithelium and stroma. However, these differences 445 may also be due to the different processing and DNA extraction protocols used for the medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

446 different sample types. The occurrence bacterial networks also revealed differences between 447 the sample types. The EB microbiota networks were more dispersed than the EF networks, 448 suggesting that the microbiota found in the EF could be more stable (Naqvi et al. 2010). Only 449 one previous study compared the EM of EF and EB, indicating that the microbiota 450 composition in EF does not fully reflect that in the EB (Liu et al. 2018).

451 To our knowledge, this is the first observational multicentre study to prospectively analyse 452 EM composition in two types of endometrial samples (EF and EB) obtained simultaneously 453 from the same patient and assess the association of the uterine microbial environment with 454 pregnancy outcomes in infertile patients undergoing IVF. The data presented here are robust 455 because both sample types were sequenced from the same bacterial 16S rRNA hypervariable 456 regions and analysed using the same bioinformatics pipeline. However, the high sensitivity of 457 this technology might detect DNA contamination, which can confound the interpretation of 458 microbiome data (Eisenhofer et al. 2019), specifically in low-biomass sites such as the . 459 Therefore, we ensured that contaminating reads, but not sample-related reads, were removed 460 from downstream analyses. For this purpose, we first classified samples as detectable or non- 461 detectable, excluding samples that clustered with and had a roughly equivalent amplicon 462 concentration to the blanks. Another strength of our work is that endometrial receptivity was 463 analysed, and personalised embryo transfer was performed to synchronise endometrial 464 receptivity with embryo development, avoiding displacement of the implantation window. 465 Additionally, all samples were collected in a hormone replacement therapy cycle, preventing 466 the potential bias introduced by different hormonal status. Hormonal therapy may have 467 influenced the endometrial microbiome, but the hormonal regimen and sampling day were 468 consistent between the sample collection analysis and embryo transfer cycles.

469 The main limitation of our work was the small number of BP and CM outcomes. Moreover, 470 transcervical collection of the samples may have influenced the endometrial microbial results. 471 Caution was taken to avoid contact of the catheter with the vaginal walls during sample 472 collection, and although cervical contamination cannot be fully discarded, there is no other 473 way to clinically access the endometrium. Moreover, it is now accepted that the female 474 reproductive tract presents a continuum of microbiota (C. Chen et al. 2017), so even if some 475 contamination from the cervix was carried over into the endometrial samples, the resulting 476 microbial profiles would be consistent with the microbial environment of the uterine cavity. 477 Another factor to be considered is that while sample collection and microbial analysis were medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

478 performed in a cycle before embryo transfer, the microbiome may change over time.

479 CONCLUSIONS

480 We conclude that the presence of pathogenic bacteria such as Atopobium, Bifidobacterium, 481 Chryseobacterium, Gardnerella, Haemophilus, Klebsiella, Neisseria, Staphylococcus, and 482 Streptococcus in the endometrium together with depletion of Lactobacillus spp. is associated 483 with impaired reproductive function. These data indicate that the EM should be considered as 484 a possible emerging cause of implantation failure and/or pregnancy loss. More studies are 485 needed to analyse the mechanism of how pathogenic bacteria might affect embryo 486 implantation.

487 DECLARATIONS

488 Ethics approval and consent to participate

489 Ethical approval was given by the corresponding local Ethics Committees to the protocol 490 with reference IGX1-MIC-CS-17-05 as follows: Western Institutional Review Board (IVF- 491 Florida, USA, study code 1176556, July 17, 2017; RMA Connecticut, USA, study code 492 1177331, July 31, 2017; Dominion Fertility, USA, study code 1176555, July 22, 2017; 493 Missouri Center for Reproductive Medicine, USA, study code 1179405, October 10, 2017; 494 Pacific Centre for Reproductive Medicine, Canada, study code 1179667, December 13, 495 2017); Comite de Etica de la Investigación con Medicamentos del Hospital Universitario de 496 la Princesa (ProcreaTec, Spain, register number 3133, July 13, 2017); Comite de Etica de la 497 Investigacion Costa del Sol (Clinica Fertia, Spain, code 006_jun17_PI-ERA-Microbioma, 498 June 29, 2017); Comite de Bioetica del Instituto de Investigaciones Clinicas Rosario 499 (Gestanza Medicina Reproductiva, Argentina, March 27, 2018); Comite de Etica en 500 Investigación Centro de Educacion Medica e Investigaciones Clinicas “Norberto Quirno” 501 (Pregna Medicina Reproductiva, Argentina, October 13, 2017); Human Medical Research 502 Ethics Committee (HMREC) for Private Healthcare and Research Centres Malaysia (Alpha 503 IVF & Women’s Specialists Centre, Malaysia, October 3, 2017); Oak Clinic Group’s Ethics 504 Committee (Oak Clinic Sumiyoshi, Japan, May 29, 2017); Comite de Etica en Investigación 505 New Hope Fertility Center (New Hope Fertility Center, Mexico, Register number RA-2017- 506 04, June 9, 2017); Uskudar Universitesi Girisimsel Olamayan Arastirmalar Etik Kurulum 507 (Bahceci Group, Turkey, January 25, 2018). All participants provided written informed 508 consent. Data were monitored by clinical research associates through on-site and/or remote medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

509 visits, and the database was properly curated by the data manager. All queries were duly 510 solved and closed.

511 Consent for publication

512 Not applicable

513 Availability of data and materials

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

515 PRJNA691300).

516 Competing interests

517 IM, DPV, MGM, DB, CG, DV, and CS are partially employed by Igenomix R&D. The rest

518 of the authors declare that they have no competing interests.

519 Funding

520 This study was supported by the Igenomix Foundation. IGG was supported by a Formación

521 de Profesorado Universitario grant (FPU15/01923) from the Spanish Ministry of Education.

522 DB is supported by Torres-Quevedo grant (PTQ-16-08454) from the Spanish Ministry of

523 Economy and Competitiveness. FV was supported by the Miguel Servet Program Type II of

524 ISCIII (CPII18/00020) and the FIS project (PI18/00957). CS was supported by the Spanish

525 Government MINECO/FEDER (grant RTI2018-094946-B-I00), the Valencian Innovation

526 Council (ref. PROMETEO/2018/161) and the European Union’s Horizon 2020 Framework

527 Programme for Research and Innovation under grant agreement number 874867.

528 Acknowledgements

529 We thank Sheila M. Cherry, PhD, ELS, President and Senior Editor from Fresh Eyes Editing

530 LLC, for her excellent work editing this manuscript.

531 Author contributions medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

532 IM and CS contributed to the conception, design, analysis and interpretation of the data. IGG

533 and DPV contributed to the acquisition, analysis and interpretation of the data. MGM

534 participated in the acquisition and analysis of the data. MB, MJB, ST, EP, MD, MWL, GM,

535 MA, ML, AI, MPO, AC, and KS contributed to sample collection and acquisition of data. DB

536 contributed to acquisition, analysis and interpretation of the data. CG contributed to design

537 and acquisition of data. DV contributed to the design and data analysis. FV contributed to the

538 conception and interpretation of the data. IM, IGG, and CS drafted the work. All authors have

539 substantively revised the manuscript and approved the submitted version. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

540 REFERENCES

541 Aitchison, J. 1986. The statistical analysis of compositional data. Chapman & Hall, Ltd. 542 Almonacid, D. E., L. Kraal, F. J. Ossandon, Y. V. Budovskaya, J. P. Cardenas, E. M. Bik, A. 543 D. Goddard, J. Richman, and Z. S. Apte. 2017. "16S rRNA gene sequencing and 544 healthy reference ranges for 28 clinically relevant microbial taxa from the human gut 545 microbiome." PLoS One 12 (5): e0176555. 546 https://doi.org/10.1371/journal.pone.0176555. 547 https://www.ncbi.nlm.nih.gov/pubmed/28467461. 548 Baker, J. M., D. M. Chase, and M. M. Herbst-Kralovetz. 2018. "Uterine Microbiota: 549 Residents, Tourists, or Invaders?" Front Immunol 9: 208. 550 https://doi.org/10.3389/fimmu.2018.00208. 551 https://www.ncbi.nlm.nih.gov/pubmed/29552006. 552 Barton, H. A., N. M. Taylor, B. R. Lubbers, and A. C. Pemberton. 2006. "DNA extraction 553 from low-biomass carbonate rock: an improved method with reduced contamination 554 and the low-biomass contaminant database." J Microbiol Methods 66 (1): 21-31. 555 https://doi.org/10.1016/j.mimet.2005.10.005. 556 https://www.ncbi.nlm.nih.gov/pubmed/16305811. 557 Benner, M., G. Ferwerda, I. Joosten, and R. G. van der Molen. 2018. "How uterine 558 microbiota might be responsible for a receptive, fertile endometrium." Hum Reprod 559 Update. https://doi.org/10.1093/humupd/dmy012. 560 https://www.ncbi.nlm.nih.gov/pubmed/29668899. 561 Bhaskar, M. M., S. Sistla, and S. Kumaravel. 2017. "A case of pyometrocolpos with 562 Bifidobacterium species." Anaerobe 44: 48-50. 563 https://doi.org/10.1016/j.anaerobe.2017.01.007. 564 https://www.ncbi.nlm.nih.gov/pubmed/28108392. 565 Bolyen, E., J. R. Rideout, M. R. Dillon, N. A. Bokulich, C. C. Abnet, G. A. Al-Ghalith, H. 566 Alexander, E. J. Alm, M. Arumugam, F. Asnicar, Y. Bai, J. E. Bisanz, K. Bittinger, 567 A. Brejnrod, C. J. Brislawn, C. T. Brown, B. J. Callahan, A. M. Caraballo-Rodríguez, 568 J. Chase, E. K. Cope, R. Da Silva, C. Diener, P. C. Dorrestein, G. M. Douglas, D. M. 569 Durall, C. Duvallet, C. F. Edwardson, M. Ernst, M. Estaki, J. Fouquier, J. M. 570 Gauglitz, S. M. Gibbons, D. L. Gibson, A. Gonzalez, K. Gorlick, J. Guo, B. Hillmann, 571 S. Holmes, H. Holste, C. Huttenhower, G. A. Huttley, S. Janssen, A. K. Jarmusch, L. 572 Jiang, B. D. Kaehler, K. B. Kang, C. R. Keefe, P. Keim, S. T. Kelley, D. Knights, I. 573 Koester, T. Kosciolek, J. Kreps, M. G. I. Langille, J. Lee, R. Ley, Y. X. Liu, E. 574 Loftfield, C. Lozupone, M. Maher, C. Marotz, B. D. Martin, D. McDonald, L. J. 575 McIver, A. V. Melnik, J. L. Metcalf, S. C. Morgan, J. T. Morton, A. T. Naimey, J. A. 576 Navas-Molina, L. F. Nothias, S. B. Orchanian, T. Pearson, S. L. Peoples, D. Petras, 577 M. L. Preuss, E. Pruesse, L. B. Rasmussen, A. Rivers, M. S. Robeson, P. Rosenthal, 578 N. Segata, M. Shaffer, A. Shiffer, R. Sinha, S. J. Song, J. R. Spear, A. D. Swafford, L. 579 R. Thompson, P. J. Torres, P. Trinh, A. Tripathi, P. J. Turnbaugh, S. Ul-Hasan, J. J. J. 580 van der Hooft, F. Vargas, Y. Vázquez-Baeza, E. Vogtmann, M. von Hippel, W. 581 Walters, Y. Wan, M. Wang, J. Warren, K. C. Weber, C. H. D. Williamson, A. D. 582 Willis, Z. Z. Xu, J. R. Zaneveld, Y. Zhang, Q. Zhu, R. Knight, and J. G. Caporaso. 583 2019. "Reproducible, interactive, scalable and extensible microbiome data science 584 using QIIME 2." Nat Biotechnol 37 (8): 852-857. https://doi.org/10.1038/s41587-019- 585 0209-9. https://www.ncbi.nlm.nih.gov/pubmed/31341288. 586 Calle, M. L. 2019. "Statistical Analysis of Metagenomics Data." Genomics Inform 17 (1): e6. 587 https://doi.org/10.5808/GI.2019.17.1.e6. 588 https://www.ncbi.nlm.nih.gov/pubmed/30929407. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

589 Chen, C., X. Song, W. Wei, H. Zhong, J. Dai, Z. Lan, F. Li, X. Yu, Q. Feng, Z. Wang, H. 590 Xie, X. Chen, C. Zeng, B. Wen, L. Zeng, H. Du, H. Tang, C. Xu, Y. Xia, H. Xia, H. 591 Yang, J. Wang, L. Madsen, S. Brix, K. Kristiansen, X. Xu, J. Li, R. Wu, and H. Jia. 592 2017. "The microbiota continuum along the female reproductive tract and its relation 593 to uterine-related diseases." Nat Commun 8 (1): 875. https://doi.org/10.1038/s41467- 594 017-00901-0. https://www.ncbi.nlm.nih.gov/pubmed/29042534. 595 Chen, Y., Z. Hong, W. Wang, L. Gu, H. Gao, L. Qiu, and W. Di. 2019. "Association between 596 the vaginal microbiome and high-risk human papillomavirus infection in pregnant 597 Chinese women." BMC Infect Dis 19 (1): 677. https://doi.org/10.1186/s12879-019- 598 4279-6. https://www.ncbi.nlm.nih.gov/pubmed/31370796. 599 Cicinelli, E., D. De Ziegler, R. Nicoletti, G. Colafiglio, N. Saliani, L. Resta, D. Rizzi, and D. 600 De Vito. 2008. "Chronic endometritis: correlation among hysteroscopic, histologic, 601 and bacteriologic findings in a prospective trial with 2190 consecutive office 602 hysteroscopies." Fertil Steril 89 (3): 677-84. 603 https://doi.org/10.1016/j.fertnstert.2007.03.074. 604 https://www.ncbi.nlm.nih.gov/pubmed/17531993. 605 Davis, N. M., D. M. Proctor, S. P. Holmes, D. A. Relman, and B. J. Callahan. 2018. "Simple 606 statistical identification and removal of contaminant sequences in marker-gene and 607 metagenomics data." Microbiome 6 (1): 226. https://doi.org/10.1186/s40168-018- 608 0605-2. https://www.ncbi.nlm.nih.gov/pubmed/30558668. 609 Díaz-Gimeno, P., J. A. Horcajadas, J. A. Martínez-Conejero, F. J. Esteban, P. Alamá, A. 610 Pellicer, and C. Simón. 2011. "A genomic diagnostic tool for human endometrial 611 receptivity based on the transcriptomic signature." Fertil Steril 95 (1): 50-60, 60.e1- 612 15. https://doi.org/10.1016/j.fertnstert.2010.04.063. 613 https://www.ncbi.nlm.nih.gov/pubmed/20619403. 614 Eisenhofer, R., J. J. Minich, C. Marotz, A. Cooper, R. Knight, and L. S. Weyrich. 2019. 615 "Contamination in Low Microbial Biomass Microbiome Studies: Issues and 616 Recommendations." Trends Microbiol 27 (2): 105-117. 617 https://doi.org/10.1016/j.tim.2018.11.003. 618 https://www.ncbi.nlm.nih.gov/pubmed/30497919. 619 Fettweis, J. M., M. G. Serrano, J. P. Brooks, D. J. Edwards, P. H. Girerd, H. I. Parikh, B. 620 Huang, T. J. Arodz, L. Edupuganti, A. L. Glascock, J. Xu, N. R. Jimenez, S. C. 621 Vivadelli, S. S. Fong, N. U. Sheth, S. Jean, V. Lee, Y. A. Bokhari, A. M. Lara, S. D. 622 Mistry, R. A. Duckworth, S. P. Bradley, V. N. Koparde, X. V. Orenda, S. H. Milton, 623 S. K. Rozycki, A. V. Matveyev, M. L. Wright, S. V. Huzurbazar, E. M. Jackson, E. 624 Smirnova, J. Korlach, Y. C. Tsai, M. R. Dickinson, J. L. Brooks, J. I. Drake, D. O. 625 Chaffin, A. L. Sexton, M. G. Gravett, C. E. Rubens, N. R. Wijesooriya, K. D. 626 Hendricks-Muñoz, K. K. Jefferson, J. F. Strauss, and G. A. Buck. 2019. "The vaginal 627 microbiome and preterm birth." Nat Med 25 (6): 1012-1021. 628 https://doi.org/10.1038/s41591-019-0450-2. 629 https://www.ncbi.nlm.nih.gov/pubmed/31142849. 630 Franasiak, J. M., and R. T. Scott. 2015. "Introduction: Microbiome in human reproduction." 631 Fertil Steril 104 (6): 1341-3. https://doi.org/10.1016/j.fertnstert.2015.10.021. 632 https://www.ncbi.nlm.nih.gov/pubmed/26515381. 633 Freitas, A. C., and J. E. Hill. 2017. "Quantification, isolation and characterization of 634 Bifidobacterium from the vaginal of reproductive aged women." 635 Anaerobe 47: 145-156. https://doi.org/10.1016/j.anaerobe.2017.05.012. 636 https://www.ncbi.nlm.nih.gov/pubmed/28552417. 637 Garcia-Grau, I., D. Perez-Villaroya, D. Bau, M. Gonzalez-Monfort, F. Vilella, I. Moreno, and 638 C. Simon. 2019. "Taxonomical and Functional Assessment of the Endometrial medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

639 Microbiota in A Context of Recurrent Reproductive Failure: A Case Report." 640 Pathogens 8 (4). https://doi.org/10.3390/pathogens8040205. 641 https://www.ncbi.nlm.nih.gov/pubmed/31653041. 642 Glassing, A., S. E. Dowd, S. Galandiuk, B. Davis, and R. J. Chiodini. 2016. "Inherent 643 bacterial DNA contamination of extraction and sequencing reagents may affect 644 interpretation of microbiota in low bacterial biomass samples." Gut Pathog 8: 24. 645 https://doi.org/10.1186/s13099-016-0103-7. 646 https://www.ncbi.nlm.nih.gov/pubmed/27239228. 647 Gloor, G. B., J. M. Macklaim, V. Pawlowsky-Glahn, and J. J. Egozcue. 2017. "Microbiome 648 Datasets Are Compositional: And This Is Not Optional." Front Microbiol 8: 2224. 649 https://doi.org/10.3389/fmicb.2017.02224. 650 https://www.ncbi.nlm.nih.gov/pubmed/29187837. 651 Grahn, N., M. Olofsson, K. Ellnebo-Svedlund, H. J. Monstein, and J. Jonasson. 2003. 652 "Identification of mixed bacterial DNA contamination in broad-range PCR 653 amplification of 16S rDNA V1 and V3 variable regions by pyrosequencing of cloned 654 amplicons." FEMS Microbiol Lett 219 (1): 87-91. https://doi.org/10.1016/S0378- 655 1097(02)01190-4. https://www.ncbi.nlm.nih.gov/pubmed/12594028. 656 Hashimoto, T., and K. Kyono. 2019. "Does dysbiotic endometrium affect blastocyst 657 implantation in IVF patients?" J Assist Reprod Genet 36 (12): 2471-2479. 658 https://doi.org/10.1007/s10815-019-01630-7. 659 https://www.ncbi.nlm.nih.gov/pubmed/31741256. 660 Inc., Plotly Technologies. 2015. Collaborative data science. Montréal, QC Plotly 661 Technologies Inc. 662 Kitaya, K., H. Matsubayashi, K. Yamaguchi, R. Nishiyama, Y. Takaya, T. Ishikawa, T. 663 Yasuo, and H. Yamada. 2016. "Chronic Endometritis: Potential Cause of Infertility 664 and Obstetric and Neonatal Complications." Am J Reprod Immunol 75 (1): 13-22. 665 https://doi.org/10.1111/aji.12438. https://www.ncbi.nlm.nih.gov/pubmed/26478517. 666 Kitaya, K., Y. Nagai, W. Arai, Y. Sakuraba, and T. Ishikawa. 2019. "Characterization of 667 Microbiota in Endometrial Fluid and Vaginal Secretions in Infertile Women with 668 Repeated Implantation Failure." Mediators Inflamm 2019: 4893437. 669 https://doi.org/10.1155/2019/4893437. 670 https://www.ncbi.nlm.nih.gov/pubmed/31249472. 671 Kitaya, K., T. Takeuchi, S. Mizuta, H. Matsubayashi, and T. Ishikawa. 2018. "Endometritis: 672 new time, new concepts." Fertil Steril 110 (3): 344-350. 673 https://doi.org/10.1016/j.fertnstert.2018.04.012. 674 https://www.ncbi.nlm.nih.gov/pubmed/29960704. 675 Koedooder, R., S. Mackens, A. Budding, D. Fares, C. Blockeel, J. Laven, and S. 676 Schoenmakers. 2019. "Identification and evaluation of the microbiome in the female 677 and male reproductive tracts." Hum Reprod Update 25 (3): 298-325. 678 https://doi.org/10.1093/humupd/dmy048. 679 https://www.ncbi.nlm.nih.gov/pubmed/30938752. 680 Koedooder, R., M. Singer, S. Schoenmakers, P. H. M. Savelkoul, S. A. Morré, J. D. de Jonge, 681 L. Poort, W. J. S.S Cuypers, N. G. M. Beckers, F. J. M. Broekmans, B. J. Cohlen, J. 682 E. den Hartog, K. Fleischer, C. B. Lambalk, J. M. J.S Smeenk, A. E. Budding, and J. 683 S. E. Laven. 2019. "The vaginal microbiome as a predictor for outcome of in vitro 684 fertilization with or without intracytoplasmic sperm injection: a prospective study." 685 Hum Reprod 34 (6): 1042-1054. https://doi.org/10.1093/humrep/dez065. 686 https://www.ncbi.nlm.nih.gov/pubmed/31119299. 687 Kyono, K., T. Hashimoto, S. Kikuchi, Y. Nagai, and Y. Sakuraba. 2019. "A pilot study and 688 case reports on endometrial microbiota and pregnancy outcome: An analysis using medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

689 16S rRNA gene sequencing among IVF patients, and trial therapeutic intervention for 690 dysbiotic endometrium." Reprod Med Biol 18 (1): 72-82. 691 https://doi.org/10.1002/rmb2.12250. 692 https://www.ncbi.nlm.nih.gov/pubmed/30655724. 693 Kyono, K., T. Hashimoto, Y. Nagai, and Y. Sakuraba. 2018. "Analysis of endometrial 694 microbiota by 16S ribosomal RNA gene sequencing among infertile patients: a single- 695 center pilot study." Reprod Med Biol 17 (3): 297-306. 696 https://doi.org/10.1002/rmb2.12105. 697 https://www.ncbi.nlm.nih.gov/pubmed/30013432. 698 Lauder, A. P., A. M. Roche, S. Sherrill-Mix, A. Bailey, A. L. Laughlin, K. Bittinger, R. Leite, 699 M. A. Elovitz, S. Parry, and F. D. Bushman. 2016. "Comparison of placenta samples 700 with contamination controls does not provide evidence for a distinct placenta 701 microbiota." Microbiome 4 (1): 29. https://doi.org/10.1186/s40168-016-0172-3. 702 https://www.ncbi.nlm.nih.gov/pubmed/27338728. 703 Laurence, M., C. Hatzis, and D. E. Brash. 2014. "Common contaminants in next-generation 704 sequencing that hinder discovery of low-abundance microbes." PLoS One 9 (5): 705 e97876. https://doi.org/10.1371/journal.pone.0097876. 706 https://www.ncbi.nlm.nih.gov/pubmed/24837716. 707 Li, M., B. Wang, M. Zhang, M. Rantalainen, S. Wang, H. Zhou, Y. Zhang, J. Shen, X. Pang, 708 H. Wei, Y. Chen, H. Lu, J. Zuo, M. Su, Y. Qiu, W. Jia, C. Xiao, L. M. Smith, S. 709 Yang, E. Holmes, H. Tang, G. Zhao, J. K. Nicholson, L. Li, and L. Zhao. 2008. 710 "Symbiotic gut microbes modulate human metabolic phenotypes." Proc Natl Acad Sci 711 U S A 105 (6): 2117-22. https://doi.org/10.1073/pnas.0712038105. 712 https://www.ncbi.nlm.nih.gov/pubmed/18252821. 713 Libby, E. K., K. E. Pascal, E. Mordechai, M. E. Adelson, and J. P. Trama. 2008. "Atopobium 714 vaginae triggers an innate immune response in an in vitro model of bacterial 715 vaginosis." Microbes Infect 10 (4): 439-46. 716 https://doi.org/10.1016/j.micinf.2008.01.004. 717 https://www.ncbi.nlm.nih.gov/pubmed/18403235. 718 Liu, Y., K. K. Wong, E. Y. Ko, X. Chen, J. Huang, S. K. Tsui, T. C. Li, and S. S. Chim. 719 2018. "Systematic Comparison of Bacterial Colonization of Endometrial Tissue and 720 Fluid Samples in Recurrent Miscarriage Patients: Implications for Future Endometrial 721 Microbiome Studies." Clin Chem 64 (12): 1743-1752. 722 https://doi.org/10.1373/clinchem.2018.289306. 723 https://www.ncbi.nlm.nih.gov/pubmed/30237148. 724 Miles, S. M., B. L. Hardy, and D. S. Merrell. 2017. "Investigation of the microbiota of the 725 reproductive tract in women undergoing a total hysterectomy and bilateral salpingo- 726 oopherectomy." Fertil Steril 107 (3): 813-820.e1. 727 https://doi.org/10.1016/j.fertnstert.2016.11.028. 728 https://www.ncbi.nlm.nih.gov/pubmed/28069180. 729 Mitchell, C. M., A. Haick, E. Nkwopara, R. Garcia, M. Rendi, K. Agnew, D. N. Fredricks, 730 and D. Eschenbach. 2015. "Colonization of the upper genital tract by vaginal bacterial 731 species in nonpregnant women." Am J Obstet Gynecol 212 (5): 611.e1-9. 732 https://doi.org/10.1016/j.ajog.2014.11.043. 733 https://www.ncbi.nlm.nih.gov/pubmed/25524398. 734 Moreno, I., E. Cicinelli, I. Garcia-Grau, M. Gonzalez-Monfort, D. Bau, F. Vilella, D. De 735 Ziegler, L. Resta, D. Valbuena, and C. Simon. 2018. "The diagnosis of chronic 736 endometritis in infertile asymptomatic women: a comparative study of histology, 737 microbial cultures, hysteroscopy, and molecular ." Am J Obstet Gynecol medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

738 218 (6): 602.e1-602.e16. https://doi.org/10.1016/j.ajog.2018.02.012. 739 https://www.ncbi.nlm.nih.gov/pubmed/29477653. 740 Moreno, I., F. M. Codoñer, F. Vilella, D. Valbuena, J. F. Martinez-Blanch, J. Jimenez- 741 Almazán, R. Alonso, P. Alamá, J. Remohí, A. Pellicer, D. Ramon, and C. Simon. 742 2016. "Evidence that the endometrial microbiota has an effect on implantation success 743 or failure." Am J Obstet Gynecol 215 (6): 684-703. 744 https://doi.org/10.1016/j.ajog.2016.09.075. 745 https://www.ncbi.nlm.nih.gov/pubmed/27717732. 746 Moreno, I., I. Garcia-Grau, D. Bau, D. Perez-Villaroya, M. Gonzalez-Monfort, F. Vilella, R. 747 Romero, and C. Simón. 2020. "The first glimpse of the endometrial microbiota 748 in early pregnancy." Am J Obstet Gynecol 222 (4): 296-305. 749 https://doi.org/10.1016/j.ajog.2020.01.031. 750 https://www.ncbi.nlm.nih.gov/pubmed/32057732. 751 Naqvi, A., H. Rangwala, A. Keshavarzian, and P. Gillevet. 2010. "Network-based modeling 752 of the human gut microbiome." Chem Biodivers 7 (5): 1040-50. 753 https://doi.org/10.1002/cbdv.200900324. 754 https://www.ncbi.nlm.nih.gov/pubmed/20491063. 755 Pathak, P., C. Trilligan, and A. Rapose. 2014. "Bifidobacterium--friend or foe? A case of 756 urinary tract infection with Bifidobacterium species." BMJ Case Rep 2014. 757 https://doi.org/10.1136/bcr-2014-205122. 758 https://www.ncbi.nlm.nih.gov/pubmed/25253483. 759 Patras, K. A., and V. Nizet. 2018. "Group B Streptococcal Maternal Colonization and 760 Neonatal Disease: Molecular Mechanisms and Preventative Approaches." Front 761 Pediatr 6: 27. https://doi.org/10.3389/fped.2018.00027. 762 https://www.ncbi.nlm.nih.gov/pubmed/29520354. 763 Pawlowsky-Glahn, Vera, Juan J Egozcue, and Raimon Tolosana-Delgado. 2015. 764 "Frontmatter." In Modelling and Analysis of Compositional Data, i-xx. 765 Peric, A., J. Weiss, N. Vulliemoz, D. Baud, and M. Stojanov. 2019. "Bacterial Colonization 766 of the Female Upper Genital Tract." Int J Mol Sci 20 (14). 767 https://doi.org/10.3390/ijms20143405. 768 https://www.ncbi.nlm.nih.gov/pubmed/31373310. 769 Peterson, J., S. Garges, M. Giovanni, P. McInnes, L. Wang, J. A. Schloss, V. Bonazzi, J. E. 770 McEwen, K. A. Wetterstrand, C. Deal, C. C. Baker, V. Di Francesco, T. K. Howcroft, 771 R. W. Karp, R. D. Lunsford, C. R. Wellington, T. Belachew, M. Wright, C. Giblin, H. 772 David, M. Mills, R. Salomon, C. Mullins, B. Akolkar, L. Begg, C. Davis, L. 773 Grandison, M. Humble, J. Khalsa, A. R. Little, H. Peavy, C. Pontzer, M. Portnoy, M. 774 H. Sayre, P. Starke-Reed, S. Zakhari, J. Read, B. Watson, M. Guyer, and NIH HMP 775 Working Group. 2009. "The NIH ." Genome Res 19 (12): 776 2317-23. https://doi.org/10.1101/gr.096651.109. 777 https://www.ncbi.nlm.nih.gov/pubmed/19819907. 778 Ravel, J., I. Moreno, and C. Simón. 2020. "Bacterial vaginosis and its association with 779 infertility, endometritis, and pelvic inflammatory disease." Am J Obstet Gynecol. 780 https://doi.org/10.1016/j.ajog.2020.10.019. 781 https://www.ncbi.nlm.nih.gov/pubmed/33091407. 782 Ruiz-Alonso, M., D. Blesa, P. Díaz-Gimeno, E. Gómez, M. Fernández-Sánchez, F. Carranza, 783 J. Carrera, F. Vilella, A. Pellicer, and C. Simón. 2013. "The endometrial receptivity 784 array for diagnosis and personalized embryo transfer as a treatment for patients with 785 repeated implantation failure." Fertil Steril 100 (3): 818-24. 786 https://doi.org/10.1016/j.fertnstert.2013.05.004. 787 https://www.ncbi.nlm.nih.gov/pubmed/23756099. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

788 Salvatier, J, TV Wiecki, and C Fonnesbeck. 2016. Probabilistic programming in Python using 789 PyMC3. PeerJ Comput Sci 2:e55. https://doi.org/10.7717/peerj-cs.55. 790 Sfakianoudis, K., M. Simopoulou, Y. Nikas, A. Rapani, N. Nitsos, K. Pierouli, A. Pappas, A. 791 Pantou, C. Markomichali, M. Koutsilieris, and K. Pantos. 2018. "Efficient treatment 792 of chronic endometritis through a novel approach of intrauterine antibiotic infusion: a 793 case series." BMC Womens Health 18 (1): 197. https://doi.org/10.1186/s12905-018- 794 0688-8. https://www.ncbi.nlm.nih.gov/pubmed/30518370. 795 Simon, J. C., J. R. Marchesi, C. Mougel, and M. A. Selosse. 2019. "Host-microbiota 796 interactions: from theory to analysis." Microbiome 7 (1): 5. 797 https://doi.org/10.1186/s40168-019-0619-4. 798 https://www.ncbi.nlm.nih.gov/pubmed/30635058. 799 Simón, C., C. Gómez, S. Cabanillas, I. Vladimirov, G. Castillón, J. Giles, K. Boynukalin, N. 800 Findikli, M. Bahçeci, I. Ortega, C. Vidal, M. Funabiki, A. Izquierdo, L. López, S. 801 Portela, N. Frantz, M. Kulmann, S. Taguchi, E. Labarta, F. Colucci, S. Mackens, X. 802 Santamaría, E. Muñoz, S. Barrera, J. A. García-Velasco, M. Fernández, M. Ferrando, 803 M. Ruiz, B. W. Mol, D. Valbuena, and ERA-RCT Study Consortium Group. 2020. "A 804 5-year multicentre randomized controlled trial comparing personalized, frozen and 805 fresh blastocyst transfer in IVF." Reprod Biomed Online 41 (3): 402-415. 806 https://doi.org/10.1016/j.rbmo.2020.06.002. 807 https://www.ncbi.nlm.nih.gov/pubmed/32723696. 808 Stinson, L. F., J. A. Keelan, and M. S. Payne. 2019. "Identification and removal of 809 contaminating microbial DNA from PCR reagents: impact on low-biomass 810 microbiome analyses." Lett Appl Microbiol 68 (1): 2-8. 811 https://doi.org/10.1111/lam.13091. https://www.ncbi.nlm.nih.gov/pubmed/30383890. 812 Swidsinski, A., H. Verstraelen, V. Loening-Baucke, S. Swidsinski, W. Mendling, and Z. 813 Halwani. 2013. "Presence of a polymicrobial endometrial in patients with 814 bacterial vaginosis." PLoS One 8 (1): e53997. 815 https://doi.org/10.1371/journal.pone.0053997. 816 https://www.ncbi.nlm.nih.gov/pubmed/23320114. 817 Tanner, M. A., B. M. Goebel, M. A. Dojka, and N. R. Pace. 1998. "Specific ribosomal DNA 818 sequences from diverse environmental settings correlate with experimental 819 contaminants." Appl Environ Microbiol 64 (8): 3110-3. 820 https://doi.org/10.1128/AEM.64.8.3110-3113.1998. 821 https://www.ncbi.nlm.nih.gov/pubmed/9687486. 822 Tissier, Henry. 1900. "Recherches sur la flore Intestinale des Nourrissons." Thesis no 529, 823 Paris. 824 Vilella, F., L. Ramirez, O. Berlanga, S. Martínez, P. Alamá, M. Meseguer, A. Pellicer, and C. 825 Simón. 2013. "PGE2 and PGF2α concentrations in human endometrial fluid as 826 biomarkers for embryonic implantation." J Clin Endocrinol Metab 98 (10): 4123-32. 827 https://doi.org/10.1210/jc.2013-2205. 828 https://www.ncbi.nlm.nih.gov/pubmed/23979956. 829 Virtanen, P., R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. 830 Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. 831 Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. 832 J. Carey, İ Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. 833 Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, 834 F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors. 2020. "SciPy 1.0: 835 fundamental algorithms for scientific computing in Python." Nat Methods 17 (3): 261- 836 272. https://doi.org/10.1038/s41592-019-0686-2. 837 https://www.ncbi.nlm.nih.gov/pubmed/32015543. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

838 Walther-António, M. R., J. Chen, F. Multinu, A. Hokenstad, T. J. Distad, E. H. Cheek, G. L. 839 Keeney, D. J. Creedon, H. Nelson, A. Mariani, and N. Chia. 2016. "Potential 840 contribution of the uterine microbiome in the development of endometrial cancer." 841 Genome Med 8 (1): 122. https://doi.org/10.1186/s13073-016-0368-y. 842 https://www.ncbi.nlm.nih.gov/pubmed/27884207. 843 Wang, Q., G. M. Garrity, J. M. Tiedje, and J. R. Cole. 2007. "Naive Bayesian classifier for 844 rapid assignment of rRNA sequences into the new bacterial taxonomy." Appl Environ 845 Microbiol 73 (16): 5261-7. https://doi.org/10.1128/AEM.00062-07. 846 https://www.ncbi.nlm.nih.gov/pubmed/17586664. 847 Weyrich, L. S., A. G. Farrer, R. Eisenhofer, L. A. Arriola, J. Young, C. A. Selway, M. 848 Handsley-Davis, C. J. Adler, J. Breen, and A. Cooper. 2019. "Laboratory 849 contamination over time during low-biomass sample analysis." Mol Ecol Resour 19 850 (4): 982-996. https://doi.org/10.1111/1755-0998.13011. 851 https://www.ncbi.nlm.nih.gov/pubmed/30887686. 852

853 FIGURE LEGENDS

854 Figure 1. Flowchart of the study population. Of all patients assessed for eligibility (n = 855 452), 44 were excluded from the analysis and 66 were lost to follow-up. Thus, 342 patients 856 were ultimately included in our assessment of the impact of the endometrial microbiome on 857 pregnancy outcomes. Abbreviations: BMI: body mass index; ERA: endometrial receptivity 858 analysis; ET: embryo transfer; HRT: hormonal replacement therapy.

859 Figure 2. Distribution of sequencing data. PCA showing the clustering of the (A) 860 endometrial fluid samples (n=336) and (B) endometrial biopsy samples (n=296) and their 861 corresponding blank controls, based on quality parameters such as percentage of empty reads, 862 dispersion index for each sample and the ratio between the filtered and mapped reads. 863 Samples are coloured using a filtered/mapped reads threshold of 0.65 for EF and 0.7 for EB. 864 Abbreviations: BP, biochemical pregnancy; EP, ectopic pregnancy; LB, live birth; CM, 865 clinical miscarriage; NP, no pregnancy.

866 Figure 3. Co-occurrence bacterial networks in endometrial fluid (A) and endometrial 867 biopsy (B) samples. Each network was created by computing the co-occurring bacteria with 868 significant Pearson correlation coefficients. Samples from all reproductive outcomes are 869 represented. Node properties: (i) circle size, proportional to the normalised and standardised 870 bacterial relative abundances; (ii) colour, communities as retrieved by the Louvain algorithm. 871 Edge properties: (i) thickness, proportional to p-value of Pearson correlation coefficient, from 872 the most significant (thicker) to the less significant (thinner); (ii) colour, red for negative, and 873 grey for positive Pearson correlation coefficients. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

874 Figure 4. Co-occurrence bacterial networks associated with reproductive outcomes. Co- 875 occurrence bacterial networks in (A) endometrial fluid samples and (B) endometrial biopsy 876 samples for each ART outcome. Each network was created by computing the co-occurring 877 bacterial communities with significant Pearson correlation coefficients. Node properties: (i) 878 circle size, proportional to the normalised and standardised bacterial relative abundances; (ii) 879 colour, communities as retrieved by the Louvain algorithm. Edge properties: (i) thickness, 880 proportional to p-value of the Pearson correlation coefficient, from the most significant 881 (thicker) to the less significant (thinner); (ii) colour, red for negative, and grey for positive 882 Pearson correlation coefficients. For association graphs, the same criteria were applied, with 883 the thickness of the circle and colour intensity being proportional to the corresponding 884 Pearson correlation coefficients. Pairs of bacteria without a circle have no significant Pearson 885 correlation coefficient. BP, biochemical pregnancy; CM, clinical miscarriage; LB, live birth; 886 NP, no pregnancy.

887 Figure 5. Lactobacillus is more abundant than other taxa in reproductive success vs 888 failure. (A) Difference between Lactobacillus and other reproductive tract taxa using z- 889 score-normalised values in endometrial fluid (left panel) and endometrial biopsy (right panel) 890 samples. (B) Predictive model showing the probability of each reproductive outcome based 891 on the EM profile. Posterior predictive distribution density plot of z-score differences 892 between Lactobacillus and other reproductive tract taxa by reproductive outcome. BP, 893 biochemical pregnancy; CM, clinical miscarriage; LB, live birth; NP, no pregnancy.

894 Figure 6. Confidence intervals and reference ranges of bacterial taxa detected in 895 patients with live birth. The endometrial microbiome from patients with a live birth was 896 analysed in (A) endometrial fluid and (B) endometrial biopsy samples to determine the 897 reference ranges for each evaluated taxon. The confidence interval displays the relative 898 abundance for all assessed patients, revealing the healthy ranges of abundance for the taxa in 899 the tested panel. The healthy distribution was used to define the 95% confidence interval (red 900 line) and taxa abundance in patients with poor reproductive outcomes—no pregnancy, 901 biochemical pregnancy, and clinical miscarriage—were comparatively represented. A taxa 902 abundance of 0 was assigned when the bacteria was not detected in a given sample. BP, 903 biochemical pregnancy; CM, clinical miscarriage; LB, live birth; NP, no pregnancy.

904 Figure 7. Pathogenic bacterial profiles significantly associated with reproductive 905 outcome. Box plots showing taxa with significant differential abundance in no pregnancy medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

906 (NP), biochemical pregnancy (BP) and clinical miscarriage (CM) compared to live birth 907 (LB). Differential abundance was calculated using the distance of each value to the upper (U) 908 or lower (L) bounds for the 95% CI in LB (Figure 5) in (A) endometrial fluid and (B) 909 endometrial biopsy samples. Only taxa with significant differential abundance, calculated 910 with a two-sided Mann-Whitney U-test, are represented in the graphs. BP, biochemical 911 pregnancy; LB, live birth; CM, clinical miscarriage; NP, no pregnancy.

912 Figure 8. Chronic endometritis profile associated with reproductive outcomes. Box plots 913 showing differential abundance in chronic endometritis-causing bacteria in no pregnancy 914 (NP), biochemical pregnancy (BP) and clinical miscarriage (CM) compared to live birth 915 (LB). Differential abundance was calculated using the distance of each value to the upper (U) 916 or lower (L) bounds for the 95% CI in LB (Figure 5) in (A) endometrial fluid and (B) 917 endometrial biopsy samples. Only taxa with significant differential abundance, calculated 918 with a two-sided Mann-Whitney U-test, are represented in the graphs. BP, biochemical 919 pregnancy; LB, live birth; CM, clinical miscarriage; NP, no pregnancy.

920 SUPPLEMENTARY FIGURES AND TABLES

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

922 samples.

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

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

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

926 Supplementary Table 3: Sequencing reads obtained from endometrial fluid and endometrial

927 biopsy samples.

928 Supplementary Table 4: Bacteria detected in endometrial fluid and endometrial biopsy

929 samples after filtering for low abundance taxa and potential contaminants.

930

931 medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. medRxiv preprint doi: https://doi.org/10.1101/2021.02.05.21251207; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.