Gut microbiome in endometriosis: a cohort study on 1,000 individuals

Gut microbiome in endometriosis: a cohort study on 1,000 individuals | 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 Gut microbiome in endometriosis: a cohort study on 1,000 individuals Inmaculada Pérez-Prieto, Eva Vargas, Eduardo Salas-Espejo, Kreete Lüll, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3894655/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 Endometriosis, defined as the presence of endometrial-like tissue outside of the uterus, is one of the most prevalent gynecological disorders. Although different theories have been proposed, its pathogenesis is not clear. Novel studies indicate that the gut microbiome may be involved in the etiology of endometriosis, nevertheless, the connection between microbes, their dysbiosis and the development of endometriosis is understudied. This case-control study analyzed the gut microbiome in women with and without endometriosis to identify microbial targets involved in the disease. Methods A subsample of 1,000 women from the Estonian Microbiome cohort, including 136 women with endometriosis and 864 control women, was analyzed. Microbial composition was determined by shotgun metagenomics and microbial functional pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Partitioning around medoids (PAM) algorithm was performed to cluster the microbial profile of the Estonian population. The alpha- and beta-diversity and differential abundance analyses were performed to assess the gut microbiome (species and KEGG orthologies [KO]) in both groups. Metagenomic reads were mapped to estrobolome-related enzymes’ sequences to study potential microbiome-estrogen metabolism axis alterations in endometriosis. Results Diversity analyses did not detect significant differences between women with and without endometriosis (Alpha-diversity: all p-values > 0.05; Beta-diversity: PERMANOVA, both R 2 0.05). No differential species or pathways were detected after multiple testing adjustment (all FDR p-values > 0.05). Sensitivity analysis excluding women at menopause (> 50 years) confirmed our results. Estrobolome-associated enzymes’ sequences reads were not significantly different between groups (all FDR p-values > 0.05). Conclusions Our findings do not provide enough evidence to support the existence of a gut microbiome-dependent mechanism directly implicated in the pathogenesis of endometriosis. To the best of our knowledge, this is the largest metagenome study on endometriosis conducted to date. Endometriosis estrobolome gut microbiota metagenomics microbiome microbiota shotgun sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 1. BACKGROUND Endometriosis, defined as the growth of endometrial-like tissue outside of the uterine cavity, is a common gynecologic disease, affecting approximately 5–10% of reproductive-aged women [ 1 ]. Endometrial lesions cause a chronic inflammatory condition associated with a wide range of reported symptoms, including dysmenorrhea, pelvic pain, dyspareunia and infertility [ 2 , 3 ]. Because these symptoms are associated with other conditions, diagnosing endometriosis requires laparoscopic examination with excisional biopsy for definitive pathology confirmation, which leads to a long diagnostic delay or frequent misdiagnosis. Although endometriosis is a widespread and burdening reproductive disorder, it has been historically understudied. Notably, proposed hypotheses such as retrograde menstruation, coelomic metaplasia, and Müllerian remnants do not explain the etiology of all the different phenotypes of endometriosis (i.e., superficial, ovarian and deep infiltrating endometriosis) [ 4 ]. Thus, endometriosis represents an important public health concern with substantial effects on the quality of life of millions of women globally [ 5 , 6 ]. The microbiome refers to the collection of genomes of the microorganisms (bacteria, viruses, fungi, protozoa and archaea) that inhabit a particular environment [ 7 ]. Particularly, the human gastrointestinal system is the most diverse microbiome within the human body, being colonized by trillions of microbes that play key roles regulating host physiological functions [ 8 , 9 ]. Indeed, a healthy balanced gut microbiome is crucial for nutrient absorption, gut epithelial barrier integrity, immune system work and other body functions [ 10 , 11 ]. Nevertheless, compositional and functional perturbations in the microbiome could lead to an unstable state called dysbiosis, which is linked to different chronic conditions such as obesity, type-2 diabetes, cancer, inflammatory bowel diseases, neurological and reproductive diseases, among others [ 12 – 16 ]. Extensive research associates the gut microbiome with circulating levels of estrogens through the secretion of β-glucuronidase, an enzyme that deconjugates estrogen into its active metabolize form [ 17 ]. The estrobolome term encapsulates the gut gene repertoire of microbial origin capable of metabolizing estrogens leading to the stimulation of epithelial proliferation throughout the female reproductive tract. Therefore, estrogen dysregulation has been shown to drive proliferative diseases such as endometriosis along with its main comorbidities like infertility and pelvic pain [ 18 ]. Indeed, the use of estrogen-progestins and progestins is the first-line medical treatment of endometriosis due to their safety, tolerability and favorable cost profile, although they are often ineffective and may lead to unwanted side effects [ 19 ]. Hence, to date, there is no cure for endometriosis and new non-hormonal therapeutic approaches are becoming increasingly necessary [ 20 ]. Considering the influence of the gut microbiome on immunomodulation and estrogen metabolism, alongside the estrogen-driven inflammatory state in endometriosis, a potential role of the gut microbiome in the pathogenesis of the disease has been proposed [ 18 , 21 ]. Recent studies suggest that gut dysbiosis induces an increment in the estrogen circulating levels, which may contribute to the hyper-estrogenic environment that promotes the progression of endometriosis [ 22 ]. Nevertheless, the connection between microbes, their dysbiosis and the development of endometriosis remains unexplored. Research on the gut microbiome in endometriosis would enable identification of novel biomarkers for noninvasive diagnostic and therapeutic approaches to identify and treat women with endometriosis earlier [ 23 ]. This study aimed to analyze and compare the gut microbiome profiles in a large cohort of women with and without endometriosis, to identify microbial signatures and pathways potentially associated with the development of the disease. We also explored the link between the estrogen metabolism and endometriosis by analyzing microbial enzymes reads of the estrobolome between women with endometriosis and controls. 2. METHODS 2.1. Study population This case-control study included 1,000 women of the Estonian Microbiome (EstMB) cohort (age = 45.61 ± 10.36 years; BMI = 25.67 ± 5.59), a volunteer-based sub-cohort of the Estonian Biobank (EstBB) created in 2017 with the objective of enriching the previous existing data with microbiome data [ 24 , 25 ]. Out of the 1000 women included in this study, two groups were established: the endometriosis group comprised of 136 patients diagnosed with this disease, and the remaining 864 individuals were grouped into the control group. Since endometriosis has been reported to have a high degree of comorbidity with other disorders [ 26 – 28 ], control women were not diagnosed with any of the most prevalent comorbidities of endometriosis (systemic lupus erythematosus, rheumatoid arthritis, autoimmune thyroiditis, celiac disease, multiple sclerosis and irritable bowel syndrome). Endometriosis was confirmed by diagnostic laparoscopy, and the cases were identified from the electronic health record data based on the ICD-10 code (N80). Self-reported data on diseases, medications, medical procedures, health-related behaviors in lifestyle, diet, physical activity, living environment, delivery mode, and stool characteristics (Bristol stool scale) were collected for each participant [ 25 ]. 2.2. Sample collection and DNA extraction The sample collection took place between 2017 and 2019. Fresh stool samples were collected by the participants immediately after defecation with a sterile Pasteur pipette, placing the samples inside a polypropylene conical 15 ml tube and stored in the fridge (+ 4°C) until transportation. The sample was subsequently delivered to the study center where it was stored at -80°C until processing. For genomic DNA isolation, microbial DNA was extracted using QIAamp DNA Stool Mini Kit (Qiagen, Germany). Approximately 200 mg of stool was used as starting material for DNA extraction following the manufacturer’s instructions. Next, the extracted DNA was quantified using Qubit 2.0 Fluorometer with dsDNA Assay Kit (Thermo Fisher Scientific). Sequencing libraries were generated using NEBNext® Ultra™ DNA Library Prep Kit for Illumina (NEB, United States) following the manufacturer’s recommendations. Briefly, 1 µg DNA per sample was used as input material, and index codes were added to attribute sequences to each sample. Each DNA sample was fragmented by sonication to an average size of 350 bp, DNA fragments were end-polished, A-tailed, and ligated with the full-length adaptor for Illumina sequencing with further PCR amplification. Finally, PCR products were purified (AMPure XP system) and libraries were analyzed for size distribution by Agilent2100. 2.3. Metagenomics analyses The shotgun metagenomic paired-end sequencing was performed by Novogene Bioinformatics Technology Co., Ltd. in the Illumina NovaSeq6000 platform resulting in 4.62 ± 0.44 Gb of data per sample (insert size, 350 bp; read length, 2 × 250 bp). Metagenomic analysis was performed as previously described [ 25 ]. Briefly, the reads were trimmed for quality and adapter sequences. The host reads that aligned to the human genome were removed with SOAP2.21 (parameters: -s 135 -l 30 -v 7 -m 200 -x 400) [ 29 ]. Quality controlled data of each sample was then used for metagenomic assembly using SOAPdenovo (v. 2.04, parameters: -d 1 -M 3 -R -u –F) [ 30 ]. Next, SOAP2.21 was used to map the clean data of each sample to the assembled scaftigs (i.e., continuous sequences within scaffolds). Unutilized paired-end reads of each sample were compiled together for mixed assembly. MetaGeneMark (v.3.38) was used to carry out gene prediction (gene length > 100 bp) based on the scaftigs (≥ 500 bp), which were assembled by single and mixed samples. CD-HIT (v.4.6) was used to dereplicate the predicted genes based on 95% identity and 90% coverage to generate the gene catalogues (parameters: -c 0.95, -G 0, -aS 0.9, -g 1, -d 0) [ 31 ]. The longest dereplicated gene was defined as the representative gene (i.e., unigene). SoapAligner [ 32 ] (v.2.21, parameters: -m 200, -x 400, identity ≥ 95%) was then used to map the clean data to the gene catalogues and to calculate the quantity of the genes for each sample. The gene abundance was calculated based on the total number of the mapped reads and the normalized gene length. The taxonomic assignment of the metagenomes was performed by comparing the marker gene homologs to a NCBI nonredundant NCBI-nr ( ftp://ftp.ncbi.nlm.nih.gov/blast/db/ ) database (201810) of taxonomically informative gene families using DIAMOND (v0.9.9.110) [ 33 ]. The homologs were annotated based on the sequence or phylogenetic similarity to the database sequences. The abundance of different taxonomic ranks was based on the gene abundance tables. As the last step, microbial functional pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) ( https://www.genome.jp/kegg/ ). 2.4. Microbiome analysis Microbiome diversity analyses were performed and visualized using phyloseq, vegan, microViz and ggplot2 packages in R. Species and KEGG Orthology groups (KOs) presented in > 10% of samples and with 0.01% or higher relative abundance were included in downstream analyses. Alpha-diversity was determined by Shannon diversity index and the observed number of unique species (i.e., observed richness), using the “diversity” and “specnumber” functions from the vegan package. Case-control comparisons were tested by linear-mixed effect models (LME) to adjust for body mass index (BMI), age, frequency of antibiotics consumption in the last year, gut emptying frequency and stool consistency, with the function “aov” from the stats package. Beta-diversity was represented using principal coordinate analysis (PCoA), based on the Bray Curtis dissimilarity, and tested for significance by Permutational analysis of variance (PERMANOVA) using the “adonis2” function from vegan package. To identify the differential microbial species between cases and controls, differential abundance analysis was performed using an Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) from the ancombc2 package. ANCOM-BC models the absolute abundances using a linear regression framework [ 34 ]. Herein, absolute abundance for identified species present in > 10% of samples with > 0.01% within each phylogenetic domain (e.g., 861 bacteria, 3 archaea, 11 eukaryota and 12 viruses) were included in the differential abundance analysis. Three taxa were unclassified at kingdom level and removed from the analysis. Additionally, ANCOM-BC was used to examine differential KOs between women with endometriosis and controls. 2.5. PAM clustering Fecal samples were clustered by applying the Partitioning Around Medoids (PAM) algorithm, also simply referred to as k-medoids, using the “pam” function from cluster package. K-medoids consists in partitioning (clustering) the data into k clusters “around medoids”, a more robust version of K-means [ 35 ]. The number of clusters that best fits the data was selected by looking at the highest Silhouette Index, since 1 denotes the best meaning that the data point is very compact within the cluster to which it belongs and far away from the other clusters. 2.6. Estrobolome-associated sequence reads analysis Protein sequences associated with the estrogen pathway (i.e., beta-glucuronidases and beta-galactosides) were downloaded from NCBI protein database [ 36 ]. Metagenomic reads were mapped to enzyme sequences using DIAMOND [ 33 ] software package with --mid-sensitive mode enabled. Alignments (reads) with < 90 percent query coverage were filtered out. The total number of aligned read pairs was finally reported for each enzyme involved in the analysis. To study potential alterations in these estrogen pathway-related enzymes in cases and controls, comparisons were performed using the ANOVA-Like Differential Expression tool (ALDEx2 v.1.28.1) [ 37 ]. 2.7. Statistical analyses Descriptive characteristics of the study participants were reported as median (q1; q3) or frequency, as appropriate. BMI, age, frequency of antibiotics consumption in the last year, gut emptying frequency and stool characteristics (Bristol stool scale) were included as potential confounders in our analyses. Five women did not record data for age, 9 for antibiotics, 2 for gut emptying frequency and 19 for stool consistency. Hence, we imputed missed data using multiple imputation method in SPSS v.28.0.1.0. For comparing non-parametric continuous data, Mann Whitney U test was performed, while categorical data was analyzed by χ 2 test. Since alterations in the gut microbiome have been widely associated with specific menopausal symptoms [ 21 ], a sensitivity analysis excluding those women with age 50 or higher was conducted to corroborate our results (n = 591). All statistical analyses were performed in R (v.4.2.1) under RStudio (v.2022.07). Statistical significance was set to 0.05 for all analyses (i.e., p-value or q-value < 0.05 for analyses using Benjamini-Hochberg false discovery rate –FDR- for multiple correction). 3. RESULTS Our study population of 1,000 women consisted of a total of 136 women with endometriosis and 864 control women. Descriptive characteristics of study participants are summarized in Table 1 . Study groups did not significantly differ for any characteristic except for age at sample collection that was significantly higher in women with endometriosis compared to controls (FDR p-value = 0.005). Table 1 Descriptive characteristics of the study participants. Characteristics Endometriosis N = 136 Control N = 864 p-value Age, median [q1; q3] 50.0 [40.8; 57.9] 45.0[36.0; 54.0] 0.005 BMI, median [q1; q3] 25.1 [22.2; 29.5] 24.2 [21.6; 28.6] 0.367 Frequency of antibiotics consumption, n (%) Not in the last year In the last year In the last 6 months In the last month In the last week 79 (58.1%) 26 (19.1%) 23 (16.9%) 7 (5.15%) 1 (0.74%) 555 (64.2%) 139 (16.1%) 128 (14.8%) 33 (3.82%) 9 (1.04%) 0.776 Gut emptying frequency, n (%) More than 2 times a day Once a day 3–6 times a week 2 times a week 1–2 times a week Less than once a week Irregular 21 (15.4%) 76 (55.9%) 29 (21.3%) 3 (2.21%) 1 (0.74%) 0 (0.00%) 6 (4.41%) 135 (15.6%) 495 (57.3%) 168 (19.4%) 12 (1.39%) 6 (0.69%) 2 (0.23%) 46 (5.32%) 0.940 Stool consistency (Bristol scale), n (%) 1 2 3 4 5 6 7 12 (8.82%) 31 (22.8%) 22 (16.2%) 30 (22.1%) 12 (8.82%) 28 (20.6%) 1 (0.74%) 63 (7.29%) 138 (16.0%) 146 (16.9%) 241 (27.9%) 114 (13.2%) 147 (17.0%) 15 (1.74%) 0.367 Note: Data presented as median [q1, q3] and frequency, as appropriate. P-values adjusted by Benjamini-Hochberg false discovery rate (FDR). Abbreviations: BMI: body mass-index 3. 1. Microbial landscape of the study cohort The microbiome composition and functionality of the Estonian study population was characterized by metagenomics shotgun sequencing as previously described [ 25 , 38 ]. KEGG orthology (KO) refers to a classification system used to assign orthologous gene groups to organisms. Orthologs are genes in different species that evolved from a common ancestral gene. KO provides a way to organize and compare biological information across different organisms based on these orthologous groups, aiding in the understanding of functional similarities and differences in molecular pathways and biological processes [ 39 , 40 ]. A total of 17,158 species and 7,869 KOs were detected, with an average of 6,942,273 species reads and 4,913,880 KOs reads per sample. After filtering by a prevalence > 10% and relative abundance > 0.01% resulted, we identified 890 species and 1629 KOs. The average relative abundance of bacteria was 98.14%, followed by 0.93% for taxa of viral origin, 0.66% for eukaryotic taxa, 0.15% for archaea and 0.13% for unclassified taxa. The most predominant phyla were Bacteroidetes (45.15%) and Firmicutes (39.86%), followed by Proteobacteria (7.07%), Actinobacteria (1.53%) and Verrucomicrobia (0.82%), among others (Fig. 1 A). The most abundant genera consisted of Bacteroides , Prevotella , Clostridium , Alistipes and Faecalibacterium (Fig. 1 B). More specifically, 890 species presented > 10% prevalence and > 0.01% of relative abundance, being Prevotella copri , Bacteroides vulgatus , Faecalibacterium prausnitzii , Bacteroides prebeius and Alistipes putredinis the most abundant microbes (Fig. 1 C). PAM clustering stratified the study population into two enterotypes ( Supplementary Figure S1 ), where P. copri and Bacteroides spp. drove the most significant differences in the gut microbiome (Fig. 2 A-B, Supplementary Figure S2 ). 72% of the samples were within the Bacteroides spp. enterotype and the remaining 28% belonged to the P. copri enterotype. The identified enterotypes were not correlated with the presence/absence of endometriosis, although they presented a negative correlation with BMI and positive with stool consistency (Fig. 2 C; Supplementary Table S1 ). 3.2. Microbial diversity analysis Next, we aimed to compare the microbial alpha- (characterized by the Shannon diversity index and observed richness) and beta-diversity between women with and without endometriosis. No significant differences between cases and controls were detected in alpha-diversity parameters, indicating that species richness was similar between both groups (all p-values > 0.05; Fig. 3 A-B). Beta-diversity analyses on the microbial and functional profile (species and KOs profile) indicated no significant dissimilarity between the groups (PERMANOVA, both R 2 0.05; Fig. 3 C-D). Interestingly, the strongest associations with beta-diversity both with species and KOs (all p-values 0.02), antibiotics frequency (both R 2 > 0.005), BMI (both R 2 = 0.004), age (both R 2 = 0.004) and gut emptying frequency (both R 2 > 0.004). 3.3. Differential abundance analysis of microbial species and KOs To detect specific species or microbial pathways that could be potentially involved in the pathogenesis of the disease, an ANCOM-BC analysis was performed on the identified species and KOs. Overall, 34 bacteria seemed to be differentially abundant between groups, for example, Clostridium sp. CAG:307 (logFC = 0.679, p = 0.006) and Acinetobacter sp. CAG:196 (logFC = 0.756, p = 0.013) were enriched in the endometriosis group, whereas Ruminococcus sp. CAG:177 (logFC=-0.398, p = 0.026) and Roseburia sp. CAG:45 (logFC=-0.324, p = 0.011) were decreased compared to controls ( Supplementary Table S2 ). Regarding functional analysis, 14 KOs associated with endometriosis, including nitrogen metabolism (logFC=-0.172, p = 0.018) or oxidative phosphorylation (logFC=-0.043, p = 0.014) that were downregulated, while 4 KOs including fatty acid biosynthesis (logFC = 0.138, p = 0.039), amino acids metabolism (logFC = 0.048, p = 0.014) and ATP-binding cassette (ABC) transporter system (logFC = 0.184, p = 0.033) were upregulated in women with endometriosis compared to controls (Fig. 4 ). However, no bacteria and KOs remained significantly different after FDR correction (all p-values > 0.05) ( Supplementary Tables S2-S3 ). 3.4. Sensitivity analysis A sensitivity analysis including only women at their reproductive age (≤ 50 years) and excluding women at menopause (> 50 years) was performed to corroborate the previous results on whole cohort. A total of 66 women with endometriosis and 525 control women were finally included. The obtained results were similar to the whole cohort results, detecting no statistically significant differences between the groups in microbial diversity and differential abundance analyses on the species and KOs profiles ( Supplementary Figure S3 and Tables S4-S5 ). 3.5. Estrobolome pathway analysis Since estrogen metabolism has been described as a keystone factor to the pathogenesis of proliferative disorders such as endometriosis, we analyzed key enzymes from the estrobolome that could lead to hyperestrogenic conditions. Thus, we compared the total read count of 156 estrogen pathway-related enzymes (including beta-glucuronidases and beta-galactosidases) between the women with and without endometriosis. No significant differences were detected in the total read counts between the cases and controls (p > 0.05, Supplementary Figure S4 ). Additionally, each enzyme was compared between groups using the ALDEx2 package (v1.28.1). We did not observe any enzyme with statistically significant differences in the read counts between the endometriosis and control women (all p-values > 0.05, Supplementary Table S6 ). Multiple testing correction was applied for all analyses. 4. DISCUSSION Endometriosis is a widespread gynecological disorder, and despite active research, there is a lack of understanding of the pathogenesis of the disease and its associated symptoms. Scientific evidence supports that estrogen drives the proliferation of endometrial-like lesions, although the reason why some women develop endometriosis and others do not remains unclear. Since the role of the gut microbiome in inflammatory and proliferative conditions as well as in estrogen metabolism is established [ 18 , 21 ], it is rational to propose an involvement of the gut microbiome in the development of the diseases. Indeed, novel studies are focusing on the gut microbial communities as important candidates for investigation in reproductive health, and several studies are associating uterine microbes with endometriosis [ 41 – 44 ]. To the best of our knowledge, our study is the first whole metagenome study (identifying bacteria, viruses, fungi, protozoa and archaea) performed in women with endometriosis, while all previous studies have exclusively analyzed the 16S rRNA gene region of the bacteria. Our study results did not identify distinct compositional or functional gut microbial profiles in women with endometriosis compared to controls, which has been observed also in a previous marker gene-based study (16S rRNA gene analysis) [ 45 ]. However, other marker gene-based studies have associated several gut microbes with endometriosis [ 46 , 47 ]. The largest study conducted up to date analyzed the gut microbiome profile of 66 women with endometriosis and 198 control women [ 46 ], where a higher abundance of Parabacteroides genus and lower Paraprevotella in endometriosis patients compared to controls were detected. In our study of 1,000 participants, we detected decrease in Paraprevotella clara and Parabacteroides sp. D26 in women with endometriosis, although these differences disappeared after multiple testing correction. A recent study compared the gut microbiome in 12 patients with moderate-to-severe endometriosis and 12 healthy women [ 47 ]. Although they did not describe any statistically significant differences in alpha-diversity, several genera such as Blautia , Bifidobacterium , Dorea and Streptococcus , were significantly increased in the endometriosis group compared to controls, while Lachnospira and Eubacterium eligens group showed a decreased abundance in women with endometriosis. Another study built classification models with machine-learning on the vaginal and gut microbial composition to predict rASRM stages 1–2 (minimal-to-medium) vs . 3–4 (moderate-to-severe) endometriosis, and found that the microbe that contributes the most to this prediction was Anaerococcus genus [ 48 ]. In our study, species from the Anaerococcus genus, however, were not detected. Nonetheless, current studies are hardly comparable due to the different sample size and microbiome detection methods, proving contradicting and inconclusive results. Importantly, contrastingly to our study where we analyzed species level by shotgun sequencing, the previous studies performed a 16S rRNA gene analysis, which limits a reliable taxonomic assignment to genus level. Recently, a higher frequency of Fusobacterium in both the endometria and ovarian endometriotic tissues from 79 patients with endometriosis were detected when compared to endometria from 76 control women [ 49 ]. Hence, they investigated further the pathogenic role of this bacteria in the development of endometriosis. Interestingly, we detected a higher relative abundance of Fusobacterium sp. CAG:815 in the gut in women with endometriosis, although the differences did not remain significant after adjustment for multiple comparisons. While evidence supporting the role of the endometrial transcriptome in endometriosis development is accumulating [ 50 , 51 ], a new debate is whether there are microbial pathways involved in the pathogenesis of the disease. In this context, our study identified several KOs possibly associated with the presence of endometriosis. We noted that a KO related to ABC transporters was enriched in women with endometriosis. Given the high regenerative capacity of the human endometrium at eutopic and ectopic sites, scientific evidence links the origin of endometriosis to stem cells [ 52 ] and supports the existence of endometrial cell subpopulations as candidate endometrial stem cells based on the side population phenotype [ 53 ]. This characteristic is due to the differential potential of cells to efflux the Hoechst dye via the ABC family of transporter proteins expressed within the cell membrane [ 54 ]. The ATP-binding cassette transporter G2 (ABCG2) expression analysis in samples of endometrium from patients with and without endometriosis found that ABCG2 was highly expressed in the endothelial cells of microvessels of eutopic endometria, and reduced in those of ectopic endometria except in cases of deep infiltrating endometriosis, suggesting that ABCG2 + microvessels may be crucial for the pathophysiology of deep infiltrating endometriosis [ 55 ]. Our results are in line with this hypothesis, nevertheless, further research considering the different stages of endometriosis is warranted to analyze potential alterations of the gut microbes and microbial pathways that could be hidden in early endometriosis stages. Another KO of interest in endometriosis is the long-chain saturated fatty acids biosynthesis, a metabolic pathway catalyzed by fatty acid synthase (FASN). We detected a KO related to long-chain saturated fatty acids biosynthesis more represented in women with endometriosis. In some cancer cell lines, FASN has been found to be fused with estrogen receptor, and its overexpression is a common molecular feature in hormone-sensitive cells, being regulated by both estradiol and progesterone [ 56 ]. During the menstrual cycle, FASN expression appears to be linked to endometrial cell proliferation [ 57 , 58 ]. Thus, inhibiting fatty acid synthase has been proposed as a therapy targeting estrogen receptor signaling in breast and endometrial cancer [ 59 ]. In fact, several studies associate the high prevalence of endometriosis with excessive lipid intake or a lipid intake imbalance and propose novel lipid metabolism-targeted approaches for the treatment of endometriosis due to the proliferative and inflammatory state of the disease [ 60 ]. We also explored the microbial genes involved in estrogen metabolism, the estrobolome, that is recognized as an important factor in the development of proliferative disorders, including endometriosis [ 18 , 61 ]. Through a comprehensive analysis of 156 estrogen pathway-related enzymes, including main candidates like beta-glucuronidases and beta-galactosidases, no significant differences in the total read counts of these enzymes between the case-control groups were detected. Our findings suggest that alterations in the abundance of these specific enzymes from the estrobolome may not directly correlate with the presence of endometriosis in our studied cohort. Nevertheless, the estrogen-estrobolome-endometriosis axis is complex and our study results cannot rule out its importance in the disease development, which warrants further research. Our study provides pioneering results about the gut microbiome composition and association with endometriosis on a large-scale study population, however, it has several limitations that should be highlighted. First, the detection power in our case-control study might have been influenced by including different subtypes of endometriosis. Endometriosis is defined as a heterogeneous disease broadly characterized into three phenotypes with different grade of severity: from superficial peritoneal as the least severe form, to ovarian and deep infiltrating endometriosis, the last being the most severe phenotype [ 4 ]. Since the inclusion of the three phenotypes could mask the presence of microbial alterations in the most severe forms, additional analyses on the different subtypes are needed to confirm our results. Furthermore, hormonal imbalance has been demonstrated to have a negative impact on the gut microbiome, while it has been reported that hormonal treatment reverses the gut microbiome dysbiosis in reproductive disorders [ 62 ]. Since the use of estrogen-progestins and progestins is the first-line medical treatment in endometriosis [ 19 ], patients with hormonal treatment may present similar gut microbial profiles than those without the disease. Hence, more studies on women with active endometriosis and no hormonal treatment are warranted to unravel the complex bidirectional relationship between the gut microbiome and endometriosis. 5. CONCLUSIONS The molecular mechanisms underlying the pathogenesis of endometriosis are not yet fully understood, which presents a challenge in its diagnosis and treatment. In this context, the gut microbiome emerges as a potential diagnostic tool and therapeutic target. We present the largest whole metagenome study on endometriosis so far, however our study findings do not provide enough evidence to support the existence of a gut microbiome-dependent mechanism implicated in the pathogenesis of endometriosis. Further research, especially involving large-scale study populations with active endometriosis and without hormonal treatment, is crucial to better understand the endometriosis-associated microbiome, and to unravel its potential for diagnosis and treatment approaches. Abbreviations ABC ATP-binding cassette ANCOM-BC Analysis of Compositions of Microbiomes with Bias Correction BMI body mass index FASN fatty acid synthase KEGG Kyoto Encyclopedia of Genes and Genomes KO KEGG Orthology group LME linear-mixed effect models PAM Partitioning Around Medoids PCoA Principal coordinates analysis PERMANOVA Permutational analysis of variance Declarations Ethics approval and consent to participate All participants included in the EstMB provided informed consent for the data and samples to be used for scientific purposes. This study was approved by the Research Ethics Committee of the University of Tartu (approval No. 266/ T10) and by the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs; approval No. 1.1-12/17). Consent for publication Not applicable. Availability of data and materials The metagenomic data analyzed during the current study are available in the European Genome-Phenome Archive database (https://www.ebi.ac.uk/ega/) under accession code EGAS00001008448. Competing interests The authors report there are no competing interests to declare. Funding This work is supported by Grants Endo-Map (PID2021-12728OB-100), ROSY (CNS2022-135999) and PRE2018-085440 funded by MCIN/AEI/10.13039/501100011033 and ERFD A way of making Europe; Estonian Research Council grants (grants No. PRG1414 to EO and PRG1076); EMBO Installation grant (No. 3573 to EO); Estonian Center of Genomics/ Roadmap II (project No. 16-0125); Horizon 2020 innovation grant (ERIN, grant No. EU952516); Grant FPU19/05561 funded by MCIN/AEI/10.13039/501100011033 and by ESF Investing in your future; Plan de Recuperación, Transformación y resiliencia, Ayudas para la recualificación del sistema universitario español, Ayudas Margarita Salas (ref. UJAR01MS). Authors’ contributions A.S., E.O. and S.A. conceived and designed the study; I.P.P., E.V., E.S.E., K.L., A.C.G., L.A.P., R.A., O.A., K.W., E.O. and S.A. performed the microbiome analyses; K.L., O.A., E.B. and E.O. participated in data generation; I.P.P., E.V., E.S.E., E.S.E., K.L., A.C.G., J.F., A.S., K.W., E.O. and S.A. interpreted the results. I.P.P., E.V., A.C.G. and S.A. drafted the manuscript. All authors reviewed the manuscript draft for important intellectual content. All authors read and approved the final manuscript. Acknowledgements This work is part of a Ph.D. thesis conducted in the Biomedicine Doctoral Studies of the University of Granada, Spain. References Taylor HS, Kotlyar AM, Flores VA. Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet (London, England). 2021;397:839–52. Khine YM, Taniguchi F, Harada T. Clinical management of endometriosis-associated infertility. Reprod Med Biol. 2016;15:217–25. Sachedina A, Todd N. 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KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353–61. Wessels JM, Domínguez MA, Leyland NA, Agarwal SK, Foster WG. Endometrial microbiota is more diverse in people with endometriosis than symptomatic controls. Sci Rep. 2021;11:18877. Hernandes C, Silveira P, Rodrigues Sereia AF, Christoff AP, Mendes H, Valter de Oliveira LF, et al. Microbiome Profile of Deep Endometriosis Patients: Comparison of Vaginal Fluid, Endometrium and Lesion. Diagnostics (Basel, Switzerland). 2020;10. Wei W, Zhang X, Tang H, Zeng L, Wu R. Microbiota composition and distribution along the female reproductive tract of women with endometriosis. Ann Clin Microbiol Antimicrob. 2020;19:15. Molina NM, Sola-Leyva A, Haahr T, Aghajanova L, Laudanski P, Castilla JA, et al. Analysing endometrial microbiome: methodological considerations and recommendations for good practice. Hum Reprod. 2021;36:859–79. Ata B, Yildiz S, Turkgeldi E, Brocal VP, Dinleyici EC, Moya A, et al. The Endobiota Study: Comparison of Vaginal, Cervical and Gut Microbiota Between Women with Stage 3/4 Endometriosis and Healthy Controls. Sci Rep. 2019;9:2204. Svensson A, Brunkwall L, Roth B, Orho-Melander M, Ohlsson B. Associations Between Endometriosis and Gut Microbiota. Reprod Sci. 2021;28:2367–77. Shan J, Ni Z, Cheng W, Zhou L, Zhai D, Sun S, et al. Gut microbiota imbalance and its correlations with hormone and inflammatory factors in patients with stage 3/4 endometriosis. Arch Gynecol Obstet. 2021;304:1363–73. Perrotta AR, Borrelli GM, Martins CO, Kallas EG, Sanabani SS, Griffith LG, et al. The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study. Reprod Sci. 2020;27:1064–73. Muraoka A, Suzuki M, Hamaguchi T, Watanabe S, Iijima K, Murofushi Y, et al. Fusobacterium infection facilitates the development of endometriosis through the phenotypic transition of endometrial fibroblasts. Sci Transl Med. 2023;15:eadd1531. Altmäe S, Esteban FJ, Stavreus-Evers A, Simón C, Giudice L, Lessey BA, et al. Guidelines for the design, analysis and interpretation of “omics” data: focus on human endometrium. Hum Reprod Update. 2014;20:12–28. Kirschen GW, Hessami K, AlAshqar A, Afrin S, Lulseged B, Borahay M. Uterine Transcriptome: Understanding Physiology and Disease Processes. Biology (Basel). 2023;12. Gargett CE. Uterine stem cells: what is the evidence? Hum Reprod Update. 2007;13:87–101. Masuda H, Matsuzaki Y, Hiratsu E, Ono M, Nagashima T, Kajitani T, et al. Stem cell-like properties of the endometrial side population: implication in endometrial regeneration. PLoS One. 2010;5:e10387. Golebiewska A, Brons NHC, Bjerkvig R, Niclou SP. Critical appraisal of the side population assay in stem cell and cancer stem cell research. Cell Stem Cell. 2011;8:136–47. Matsuzaki S, Darcha C. Adenosine triphosphate-binding cassette transporter G2 expression in endometriosis and in endometrium from patients with and without endometriosis. Fertil Steril. 2012;98:1512-20.e3. Menendez JA, Lupu R. Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat Rev Cancer. 2007;7:763–77. Pizer ES, Kurman RJ, Pasternack GR, Kuhajda FP. Expression of fatty acid synthase is closely linked to proliferation and stromal decidualization in cycling endometrium. Int J Gynecol Pathol. 1997;16:45–51. Escot C, Joyeux C, Mathieu M, Maudelonde T, Pages A, Rochefort H, et al. Regulation of fatty acid synthetase ribonucleic acid in the human endometrium during the menstrual cycle. J Clin Endocrinol Metab. 1990;70:1319–24. Lupu R, Menendez JA. Targeting fatty acid synthase in breast and endometrial cancer: An alternative to selective estrogen receptor modulators? Endocrinology. 2006;147:4056–66. Netsu S, Konno R, Odagiri K, Soma M, Fujiwara H, Suzuki M. Oral eicosapentaenoic acid supplementation as possible therapy for endometriosis. Fertil Steril. 2008;90 4 Suppl:1496–502. Pai AH-Y, Wang Y-W, Lu P-C, Wu H-M, Xu J-L, Huang H-Y. Gut Microbiome-Estrobolome Profile in Reproductive-Age Women with Endometriosis. Int J Mol Sci. 2023;24. Jiang L, Fei H, Tong J, Zhou J, Zhu J, Jin X, et al. Hormone Replacement Therapy Reverses Gut Microbiome and Serum Metabolome Alterations in Premature Ovarian Insufficiency. Front Endocrinol (Lausanne). 2021;12:794496. Supplementary Informations Supplementary Figures and Tables are not available with this version. Additional Declarations The authors declare no competing interests. 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. 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16:17:51","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3894655/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3894655/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50173990,"identity":"c8c49e20-c0b3-4df6-a91b-38061fe138f8","added_by":"auto","created_at":"2024-01-25 16:03:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1528803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial landscape in the Estonian study population.\u003c/strong\u003e Circular stacked barplots (“iris plots”) show the most relatively abundant phyla (A), genera (B) and species (C) in the study population. The outer bicolor rings indicate the endometriosis and control groups.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3894655/v1/a1aa6d8df893a2d296a41e3b.jpeg"},{"id":50173988,"identity":"04ee8dc7-9fa8-4f20-af52-275532b9931f","added_by":"auto","created_at":"2024-01-25 16:03:39","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":422672,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnterotypes identified in the Estonian study population.\u003c/strong\u003e (A, B) Relative abundance of \u003cem\u003ePrevotella copri\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e spp. within the enterotypes on the principal coordinates analysis (PCoA) plot of the species-level microbiome profile based on the Bray-Curtis dissimilarity. (C) Distribution of women with and without endometriosis within the enterotypes. The dot’s shape indicates the cluster, while the colors highlight the relative abundances (A, B) or the endometriosis and control groups (C).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3894655/v1/9da829637824648ca36cfdad.jpeg"},{"id":50174451,"identity":"40e1fdb2-3b8e-419c-86f4-6ebea57fdf7e","added_by":"auto","created_at":"2024-01-25 16:11:39","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":484092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial diversity measures in endometriosis and control groups.\u003c/strong\u003e (A, B) Alpha-diversity analysis (i.e, Shannon diversity index and observed richness). Groups comparisons indicate no significant differences (Linear-mixed effects: all p-values \u0026gt;0.05). (C, D) Beta-diversity analyses on the principal coordinates analysis (PCoA) of the species (C) and KOs (D) profile based on the Bray-Curtis dissimilarity (Adonis PERMANOVA, both R\u003csup\u003e2\u003c/sup\u003e \u0026lt;0.0007, both p-values \u0026gt;0.05).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3894655/v1/b87c60fb3965a4f4e1cddf03.jpeg"},{"id":50173991,"identity":"c8171fd6-1c9e-4028-90ed-1e0e9805712a","added_by":"auto","created_at":"2024-01-25 16:03:40","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":285662,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional differences in the microbial pathways in endometriosis and control groups.\u003c/strong\u003e Volcano plot displaying log fold change differences in the expression of KEGG orthologs derived from the ANCOM-BC model. Points in blue and red represent KEGG orthologs which were downregulated and upregulated in endometriosis and statistically significant (p \u0026lt;0.05). Points in black represent KEGG orthologs that were not differentially expressed (p \u0026gt;0.05). No KEGG orthologs remained statistically significant after Benjamini-Hochberg false discovery rate (FDR) correction (all FDR p-values \u0026gt;0.05).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3894655/v1/e14c85451df2fbfe086ef34e.jpeg"},{"id":50175104,"identity":"abbcd702-5bf1-4352-9828-f2ee1952751d","added_by":"auto","created_at":"2024-01-25 16:19:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":854853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3894655/v1/c04c8b41-b016-4bc0-a227-d5003e2ff272.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGut microbiome in endometriosis: a cohort study on 1,000 individuals\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eEndometriosis, defined as the growth of endometrial-like tissue outside of the uterine cavity, is a common gynecologic disease, affecting approximately 5\u0026ndash;10% of reproductive-aged women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Endometrial lesions cause a chronic inflammatory condition associated with a wide range of reported symptoms, including dysmenorrhea, pelvic pain, dyspareunia and infertility [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Because these symptoms are associated with other conditions, diagnosing endometriosis requires laparoscopic examination with excisional biopsy for definitive pathology confirmation, which leads to a long diagnostic delay or frequent misdiagnosis. Although endometriosis is a widespread and burdening reproductive disorder, it has been historically understudied. Notably, proposed hypotheses such as retrograde menstruation, coelomic metaplasia, and M\u0026uuml;llerian remnants do not explain the etiology of all the different phenotypes of endometriosis (i.e., superficial, ovarian and deep infiltrating endometriosis) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, endometriosis represents an important public health concern with substantial effects on the quality of life of millions of women globally [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe microbiome refers to the collection of genomes of the microorganisms (bacteria, viruses, fungi, protozoa and archaea) that inhabit a particular environment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Particularly, the human gastrointestinal system is the most diverse microbiome within the human body, being colonized by trillions of microbes that play key roles regulating host physiological functions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Indeed, a healthy balanced gut microbiome is crucial for nutrient absorption, gut epithelial barrier integrity, immune system work and other body functions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Nevertheless, compositional and functional perturbations in the microbiome could lead to an unstable state called dysbiosis, which is linked to different chronic conditions such as obesity, type-2 diabetes, cancer, inflammatory bowel diseases, neurological and reproductive diseases, among others [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtensive research associates the gut microbiome with circulating levels of estrogens through the secretion of β-glucuronidase, an enzyme that deconjugates estrogen into its active metabolize form [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The estrobolome term encapsulates the gut gene repertoire of microbial origin capable of metabolizing estrogens leading to the stimulation of epithelial proliferation throughout the female reproductive tract. Therefore, estrogen dysregulation has been shown to drive proliferative diseases such as endometriosis along with its main comorbidities like infertility and pelvic pain [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Indeed, the use of estrogen-progestins and progestins is the first-line medical treatment of endometriosis due to their safety, tolerability and favorable cost profile, although they are often ineffective and may lead to unwanted side effects [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Hence, to date, there is no cure for endometriosis and new non-hormonal therapeutic approaches are becoming increasingly necessary [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the influence of the gut microbiome on immunomodulation and estrogen metabolism, alongside the estrogen-driven inflammatory state in endometriosis, a potential role of the gut microbiome in the pathogenesis of the disease has been proposed [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Recent studies suggest that gut dysbiosis induces an increment in the estrogen circulating levels, which may contribute to the hyper-estrogenic environment that promotes the progression of endometriosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nevertheless, the connection between microbes, their dysbiosis and the development of endometriosis remains unexplored. Research on the gut microbiome in endometriosis would enable identification of novel biomarkers for noninvasive diagnostic and therapeutic approaches to identify and treat women with endometriosis earlier [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to analyze and compare the gut microbiome profiles in a large cohort of women with and without endometriosis, to identify microbial signatures and pathways potentially associated with the development of the disease. We also explored the link between the estrogen metabolism and endometriosis by analyzing microbial enzymes reads of the estrobolome between women with endometriosis and controls.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population\u003c/h2\u003e \u003cp\u003eThis case-control study included 1,000 women of the Estonian Microbiome (EstMB) cohort (age\u0026thinsp;=\u0026thinsp;45.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36 years; BMI\u0026thinsp;=\u0026thinsp;25.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.59), a volunteer-based sub-cohort of the Estonian Biobank (EstBB) created in 2017 with the objective of enriching the previous existing data with microbiome data [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Out of the 1000 women included in this study, two groups were established: the endometriosis group comprised of 136 patients diagnosed with this disease, and the remaining 864 individuals were grouped into the control group. Since endometriosis has been reported to have a high degree of comorbidity with other disorders [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], control women were not diagnosed with any of the most prevalent comorbidities of endometriosis (systemic lupus erythematosus, rheumatoid arthritis, autoimmune thyroiditis, celiac disease, multiple sclerosis and irritable bowel syndrome). Endometriosis was confirmed by diagnostic laparoscopy, and the cases were identified from the electronic health record data based on the ICD-10 code (N80). Self-reported data on diseases, medications, medical procedures, health-related behaviors in lifestyle, diet, physical activity, living environment, delivery mode, and stool characteristics (Bristol stool scale) were collected for each participant [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample collection and DNA extraction\u003c/h2\u003e \u003cp\u003eThe sample collection took place between 2017 and 2019. Fresh stool samples were collected by the participants immediately after defecation with a sterile Pasteur pipette, placing the samples inside a polypropylene conical 15 ml tube and stored in the fridge (+\u0026thinsp;4\u0026deg;C) until transportation. The sample was subsequently delivered to the study center where it was stored at -80\u0026deg;C until processing.\u003c/p\u003e \u003cp\u003eFor genomic DNA isolation, microbial DNA was extracted using QIAamp DNA Stool Mini Kit (Qiagen, Germany). Approximately 200 mg of stool was used as starting material for DNA extraction following the manufacturer\u0026rsquo;s instructions. Next, the extracted DNA was quantified using Qubit 2.0 Fluorometer with dsDNA Assay Kit (Thermo Fisher Scientific). Sequencing libraries were generated using NEBNext\u0026reg; Ultra\u0026trade; DNA Library Prep Kit for Illumina (NEB, United States) following the manufacturer\u0026rsquo;s recommendations. Briefly, 1 \u0026micro;g DNA per sample was used as input material, and index codes were added to attribute sequences to each sample. Each DNA sample was fragmented by sonication to an average size of 350 bp, DNA fragments were end-polished, A-tailed, and ligated with the full-length adaptor for Illumina sequencing with further PCR amplification. Finally, PCR products were purified (AMPure XP system) and libraries were analyzed for size distribution by Agilent2100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Metagenomics analyses\u003c/h2\u003e \u003cp\u003eThe shotgun metagenomic paired-end sequencing was performed by Novogene Bioinformatics Technology Co., Ltd. in the Illumina NovaSeq6000 platform resulting in 4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44 Gb of data per sample (insert size, 350 bp; read length, 2 \u0026times; 250 bp). Metagenomic analysis was performed as previously described [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Briefly, the reads were trimmed for quality and adapter sequences. The host reads that aligned to the human genome were removed with SOAP2.21 (parameters: -s 135 -l 30 -v 7 -m 200 -x 400) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Quality controlled data of each sample was then used for metagenomic assembly using SOAPdenovo (v. 2.04, parameters: -d 1 -M 3 -R -u \u0026ndash;F) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Next, SOAP2.21 was used to map the clean data of each sample to the assembled scaftigs (i.e., continuous sequences within scaffolds). Unutilized paired-end reads of each sample were compiled together for mixed assembly. MetaGeneMark (v.3.38) was used to carry out gene prediction (gene length\u0026thinsp;\u0026gt;\u0026thinsp;100 bp) based on the scaftigs (\u0026ge;\u0026thinsp;500 bp), which were assembled by single and mixed samples. CD-HIT (v.4.6) was used to dereplicate the predicted genes based on 95% identity and 90% coverage to generate the gene catalogues (parameters: -c 0.95, -G 0, -aS 0.9, -g 1, -d 0) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The longest dereplicated gene was defined as the representative gene (i.e., unigene). SoapAligner [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] (v.2.21, parameters: -m 200, -x 400, identity\u0026thinsp;\u0026ge;\u0026thinsp;95%) was then used to map the clean data to the gene catalogues and to calculate the quantity of the genes for each sample. The gene abundance was calculated based on the total number of the mapped reads and the normalized gene length. The taxonomic assignment of the metagenomes was performed by comparing the marker gene homologs to a NCBI nonredundant NCBI-nr (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.ncbi.nlm.nih.gov/blast/db/\u003c/span\u003e\u003cspan address=\"http://ftp://ftp.ncbi.nlm.nih.gov/blast/db/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database (201810) of taxonomically informative gene families using DIAMOND (v0.9.9.110) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The homologs were annotated based on the sequence or phylogenetic similarity to the database sequences. The abundance of different taxonomic ranks was based on the gene abundance tables. As the last step, microbial functional pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Microbiome analysis\u003c/h2\u003e \u003cp\u003eMicrobiome diversity analyses were performed and visualized using phyloseq, vegan, microViz and ggplot2 packages in R. Species and KEGG Orthology groups (KOs) presented in \u0026gt;\u0026thinsp;10% of samples and with 0.01% or higher relative abundance were included in downstream analyses. Alpha-diversity was determined by Shannon diversity index and the observed number of unique species (i.e., observed richness), using the \u0026ldquo;diversity\u0026rdquo; and \u0026ldquo;specnumber\u0026rdquo; functions from the vegan package. Case-control comparisons were tested by linear-mixed effect models (LME) to adjust for body mass index (BMI), age, frequency of antibiotics consumption in the last year, gut emptying frequency and stool consistency, with the function \u0026ldquo;aov\u0026rdquo; from the stats package. Beta-diversity was represented using principal coordinate analysis (PCoA), based on the Bray Curtis dissimilarity, and tested for significance by Permutational analysis of variance (PERMANOVA) using the \u0026ldquo;adonis2\u0026rdquo; function from vegan package.\u003c/p\u003e \u003cp\u003eTo identify the differential microbial species between cases and controls, differential abundance analysis was performed using an Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) from the ancombc2 package. ANCOM-BC models the absolute abundances using a linear regression framework [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Herein, absolute abundance for identified species present in \u0026gt;\u0026thinsp;10% of samples with \u0026gt;\u0026thinsp;0.01% within each phylogenetic domain (e.g., 861 bacteria, 3 archaea, 11 eukaryota and 12 viruses) were included in the differential abundance analysis. Three taxa were unclassified at kingdom level and removed from the analysis. Additionally, ANCOM-BC was used to examine differential KOs between women with endometriosis and controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. PAM clustering\u003c/h2\u003e \u003cp\u003eFecal samples were clustered by applying the Partitioning Around Medoids (PAM) algorithm, also simply referred to as k-medoids, using the \u0026ldquo;pam\u0026rdquo; function from cluster package. K-medoids consists in partitioning (clustering) the data into k clusters \u0026ldquo;around medoids\u0026rdquo;, a more robust version of K-means [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The number of clusters that best fits the data was selected by looking at the highest Silhouette Index, since 1 denotes the best meaning that the data point is very compact within the cluster to which it belongs and far away from the other clusters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Estrobolome-associated sequence reads analysis\u003c/h2\u003e \u003cp\u003eProtein sequences associated with the estrogen pathway (i.e., beta-glucuronidases and beta-galactosides) were downloaded from NCBI protein database [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Metagenomic reads were mapped to enzyme sequences using DIAMOND [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] software package with --mid-sensitive mode enabled. Alignments (reads) with \u0026lt;\u0026thinsp;90 percent query coverage were filtered out. The total number of aligned read pairs was finally reported for each enzyme involved in the analysis. To study potential alterations in these estrogen pathway-related enzymes in cases and controls, comparisons were performed using the ANOVA-Like Differential Expression tool (ALDEx2 v.1.28.1) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analyses\u003c/h2\u003e \u003cp\u003eDescriptive characteristics of the study participants were reported as median (q1; q3) or frequency, as appropriate. BMI, age, frequency of antibiotics consumption in the last year, gut emptying frequency and stool characteristics (Bristol stool scale) were included as potential confounders in our analyses. Five women did not record data for age, 9 for antibiotics, 2 for gut emptying frequency and 19 for stool consistency. Hence, we imputed missed data using multiple imputation method in SPSS v.28.0.1.0. For comparing non-parametric continuous data, Mann Whitney \u003cem\u003eU\u003c/em\u003e test was performed, while categorical data was analyzed by χ\u003csup\u003e2\u003c/sup\u003e test.\u003c/p\u003e \u003cp\u003eSince alterations in the gut microbiome have been widely associated with specific menopausal symptoms [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], a sensitivity analysis excluding those women with age 50 or higher was conducted to corroborate our results (n\u0026thinsp;=\u0026thinsp;591).\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed in R (v.4.2.1) under RStudio (v.2022.07). Statistical significance was set to 0.05 for all analyses (i.e., p-value or q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for analyses using Benjamini-Hochberg false discovery rate \u0026ndash;FDR- for multiple correction).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eOur study population of 1,000 women consisted of a total of 136 women with endometriosis and 864 control women. Descriptive characteristics of study participants are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Study groups did not significantly differ for any characteristic except for age at sample collection that was significantly higher in women with endometriosis compared to controls (FDR p-value\u0026thinsp;=\u0026thinsp;0.005).\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\" style=\"margin-left: calc(0%); width: 100%;\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive characteristics of the study participants.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEndometriosis\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;136\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;864\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, \u003cem\u003emedian [q1; q3]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0 [40.8; 57.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.0[36.0; 54.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, \u003cem\u003emedian [q1; q3]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.1 [22.2; 29.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.2 [21.6; 28.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequency of antibiotics consumption, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eNot in the last year\u003c/p\u003e\n \u003cp\u003eIn the last year\u003c/p\u003e\n \u003cp\u003eIn the last 6 months\u003c/p\u003e\n \u003cp\u003eIn the last month\u003c/p\u003e\n \u003cp\u003eIn the last week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e79 (58.1%)\u003c/p\u003e\n \u003cp\u003e26 (19.1%)\u003c/p\u003e\n \u003cp\u003e23 (16.9%)\u003c/p\u003e\n \u003cp\u003e7 (5.15%)\u003c/p\u003e\n \u003cp\u003e1 (0.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e555 (64.2%)\u003c/p\u003e\n \u003cp\u003e139 (16.1%)\u003c/p\u003e\n \u003cp\u003e128 (14.8%)\u003c/p\u003e\n \u003cp\u003e33 (3.82%)\u003c/p\u003e\n \u003cp\u003e9 (1.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGut emptying frequency, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMore than 2 times a day\u003c/p\u003e\n \u003cp\u003eOnce a day\u003c/p\u003e\n \u003cp\u003e3\u0026ndash;6 times a week\u003c/p\u003e\n \u003cp\u003e2 times a week\u003c/p\u003e\n \u003cp\u003e1\u0026ndash;2 times a week\u003c/p\u003e\n \u003cp\u003eLess than once a week\u003c/p\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e21 (15.4%)\u003c/p\u003e\n \u003cp\u003e76 (55.9%)\u003c/p\u003e\n \u003cp\u003e29 (21.3%)\u003c/p\u003e\n \u003cp\u003e3 (2.21%)\u003c/p\u003e\n \u003cp\u003e1 (0.74%)\u003c/p\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003cp\u003e6 (4.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e135 (15.6%)\u003c/p\u003e\n \u003cp\u003e495 (57.3%)\u003c/p\u003e\n \u003cp\u003e168 (19.4%)\u003c/p\u003e\n \u003cp\u003e12 (1.39%)\u003c/p\u003e\n \u003cp\u003e6 (0.69%)\u003c/p\u003e\n \u003cp\u003e2 (0.23%)\u003c/p\u003e\n \u003cp\u003e46 (5.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStool consistency (Bristol scale), \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e12 (8.82%)\u003c/p\u003e\n \u003cp\u003e31 (22.8%)\u003c/p\u003e\n \u003cp\u003e22 (16.2%)\u003c/p\u003e\n \u003cp\u003e30 (22.1%)\u003c/p\u003e\n \u003cp\u003e12 (8.82%)\u003c/p\u003e\n \u003cp\u003e28 (20.6%)\u003c/p\u003e\n \u003cp\u003e1 (0.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e63 (7.29%)\u003c/p\u003e\n \u003cp\u003e138 (16.0%)\u003c/p\u003e\n \u003cp\u003e146 (16.9%)\u003c/p\u003e\n \u003cp\u003e241 (27.9%)\u003c/p\u003e\n \u003cp\u003e114 (13.2%)\u003c/p\u003e\n \u003cp\u003e147 (17.0%)\u003c/p\u003e\n \u003cp\u003e15 (1.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: Data presented as median [q1, q3] and frequency, as appropriate. P-values adjusted by Benjamini-Hochberg false discovery rate (FDR). Abbreviations: BMI: body mass-index\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003e3. 1. Microbial landscape of the study cohort\u003c/h3\u003e\n\u003cp\u003eThe microbiome composition and functionality of the Estonian study population was characterized by metagenomics shotgun sequencing as previously described [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. KEGG orthology (KO) refers to a classification system used to assign orthologous gene groups to organisms. Orthologs are genes in different species that evolved from a common ancestral gene. KO provides a way to organize and compare biological information across different organisms based on these orthologous groups, aiding in the understanding of functional similarities and differences in molecular pathways and biological processes [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eA total of 17,158 species and 7,869 KOs were detected, with an average of 6,942,273 species reads and 4,913,880 KOs reads per sample. After filtering by a prevalence\u0026thinsp;\u0026gt;\u0026thinsp;10% and relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;0.01% resulted, we identified 890 species and 1629 KOs. The average relative abundance of bacteria was 98.14%, followed by 0.93% for taxa of viral origin, 0.66% for eukaryotic taxa, 0.15% for archaea and 0.13% for unclassified taxa. The most predominant phyla were \u003cem\u003eBacteroidetes\u003c/em\u003e (45.15%) and \u003cem\u003eFirmicutes\u003c/em\u003e (39.86%), followed by \u003cem\u003eProteobacteria\u003c/em\u003e (7.07%), \u003cem\u003eActinobacteria\u003c/em\u003e (1.53%) and \u003cem\u003eVerrucomicrobia\u003c/em\u003e (0.82%), among others (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). The most abundant genera consisted of \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). More specifically, 890 species presented\u0026thinsp;\u0026gt;\u0026thinsp;10% prevalence and \u0026gt;\u0026thinsp;0.01% of relative abundance, being \u003cem\u003ePrevotella copri\u003c/em\u003e, \u003cem\u003eBacteroides vulgatus\u003c/em\u003e, \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e, \u003cem\u003eBacteroides prebeius\u003c/em\u003e and \u003cem\u003eAlistipes putredinis\u003c/em\u003e the most abundant microbes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003ePAM clustering stratified the study population into two enterotypes (\u003cstrong\u003eSupplementary Figure S1\u003c/strong\u003e), where \u003cem\u003eP. copri\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e spp. drove the most significant differences in the gut microbiome (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-B, \u003cstrong\u003eSupplementary Figure S2\u003c/strong\u003e). 72% of the samples were within the \u003cem\u003eBacteroides\u003c/em\u003e spp. enterotype and the remaining 28% belonged to the \u003cem\u003eP. copri\u003c/em\u003e enterotype. The identified enterotypes were not correlated with the presence/absence of endometriosis, although they presented a negative correlation with BMI and positive with stool consistency (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC; \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Microbial diversity analysis\u003c/h2\u003e\n \u003cp\u003eNext, we aimed to compare the microbial alpha- (characterized by the Shannon diversity index and observed richness) and beta-diversity between women with and without endometriosis. No significant differences between cases and controls were detected in alpha-diversity parameters, indicating that species richness was similar between both groups (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). Beta-diversity analyses on the microbial and functional profile (species and KOs profile) indicated no significant dissimilarity between the groups (PERMANOVA, both R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0007, p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC-D). Interestingly, the strongest associations with beta-diversity both with species and KOs (all p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.004), were observed for the stool consistency (evaluated by the Bristol stool scale, both R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.02), antibiotics frequency (both R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.005), BMI (both R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.004), age (both R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.004) and gut emptying frequency (both R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.004).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Differential abundance analysis of microbial species and KOs\u003c/h2\u003e\n \u003cp\u003eTo detect specific species or microbial pathways that could be potentially involved in the pathogenesis of the disease, an ANCOM-BC analysis was performed on the identified species and KOs. Overall, 34 bacteria seemed to be differentially abundant between groups, for example, \u003cem\u003eClostridium\u003c/em\u003e sp. CAG:307 (logFC\u0026thinsp;=\u0026thinsp;0.679, p\u0026thinsp;=\u0026thinsp;0.006) and \u003cem\u003eAcinetobacter\u003c/em\u003e sp. CAG:196 (logFC\u0026thinsp;=\u0026thinsp;0.756, p\u0026thinsp;=\u0026thinsp;0.013) were enriched in the endometriosis group, whereas \u003cem\u003eRuminococcus\u003c/em\u003e sp. CAG:177 (logFC=-0.398, p\u0026thinsp;=\u0026thinsp;0.026) and \u003cem\u003eRoseburia\u003c/em\u003e sp. CAG:45 (logFC=-0.324, p\u0026thinsp;=\u0026thinsp;0.011) were decreased compared to controls (\u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e). Regarding functional analysis, 14 KOs associated with endometriosis, including nitrogen metabolism (logFC=-0.172, p\u0026thinsp;=\u0026thinsp;0.018) or oxidative phosphorylation (logFC=-0.043, p\u0026thinsp;=\u0026thinsp;0.014) that were downregulated, while 4 KOs including fatty acid biosynthesis (logFC\u0026thinsp;=\u0026thinsp;0.138, p\u0026thinsp;=\u0026thinsp;0.039), amino acids metabolism (logFC\u0026thinsp;=\u0026thinsp;0.048, p\u0026thinsp;=\u0026thinsp;0.014) and ATP-binding cassette (ABC) transporter system (logFC\u0026thinsp;=\u0026thinsp;0.184, p\u0026thinsp;=\u0026thinsp;0.033) were upregulated in women with endometriosis compared to controls (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). However, no bacteria and KOs remained significantly different after FDR correction (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (\u003cstrong\u003eSupplementary Tables S2-S3\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eA sensitivity analysis including only women at their reproductive age (\u0026le;\u0026thinsp;50 years) and excluding women at menopause (\u0026gt;\u0026thinsp;50 years) was performed to corroborate the previous results on whole cohort. A total of 66 women with endometriosis and 525 control women were finally included. The obtained results were similar to the whole cohort results, detecting no statistically significant differences between the groups in microbial diversity and differential abundance analyses on the species and KOs profiles (\u003cstrong\u003eSupplementary Figure S3\u003c/strong\u003e and \u003cstrong\u003eTables S4-S5\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Estrobolome pathway analysis\u003c/h2\u003e\n \u003cp\u003eSince estrogen metabolism has been described as a keystone factor to the pathogenesis of proliferative disorders such as endometriosis, we analyzed key enzymes from the estrobolome that could lead to hyperestrogenic conditions. Thus, we compared the total read count of 156 estrogen pathway-related enzymes (including beta-glucuronidases and beta-galactosidases) between the women with and without endometriosis. No significant differences were detected in the total read counts between the cases and controls (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, \u003cstrong\u003eSupplementary Figure S4\u003c/strong\u003e). Additionally, each enzyme was compared between groups using the ALDEx2 package (v1.28.1). We did not observe any enzyme with statistically significant differences in the read counts between the endometriosis and control women (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05, \u003cstrong\u003eSupplementary Table S6\u003c/strong\u003e). Multiple testing correction was applied for all analyses.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eEndometriosis is a widespread gynecological disorder, and despite active research, there is a lack of understanding of the pathogenesis of the disease and its associated symptoms. Scientific evidence supports that estrogen drives the proliferation of endometrial-like lesions, although the reason why some women develop endometriosis and others do not remains unclear. Since the role of the gut microbiome in inflammatory and proliferative conditions as well as in estrogen metabolism is established [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], it is rational to propose an involvement of the gut microbiome in the development of the diseases. Indeed, novel studies are focusing on the gut microbial communities as important candidates for investigation in reproductive health, and several studies are associating uterine microbes with endometriosis [\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, our study is the first whole metagenome study (identifying bacteria, viruses, fungi, protozoa and archaea) performed in women with endometriosis, while all previous studies have exclusively analyzed the 16S rRNA gene region of the bacteria. Our study results did not identify distinct compositional or functional gut microbial profiles in women with endometriosis compared to controls, which has been observed also in a previous marker gene-based study (16S rRNA gene analysis) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, other marker gene-based studies have associated several gut microbes with endometriosis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The largest study conducted up to date analyzed the gut microbiome profile of 66 women with endometriosis and 198 control women [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], where a higher abundance of \u003cem\u003eParabacteroides\u003c/em\u003e genus and lower \u003cem\u003eParaprevotella\u003c/em\u003e in endometriosis patients compared to controls were detected. In our study of 1,000 participants, we detected decrease in \u003cem\u003eParaprevotella clara\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e sp. D26 in women with endometriosis, although these differences disappeared after multiple testing correction. A recent study compared the gut microbiome in 12 patients with moderate-to-severe endometriosis and 12 healthy women [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Although they did not describe any statistically significant differences in alpha-diversity, several genera such as \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eDorea\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e, were significantly increased in the endometriosis group compared to controls, while \u003cem\u003eLachnospira\u003c/em\u003e and \u003cem\u003eEubacterium eligens\u003c/em\u003e group showed a decreased abundance in women with endometriosis. Another study built classification models with machine-learning on the vaginal and gut microbial composition to predict rASRM stages 1\u0026ndash;2 (minimal-to-medium) \u003cem\u003evs\u003c/em\u003e. 3\u0026ndash;4 (moderate-to-severe) endometriosis, and found that the microbe that contributes the most to this prediction was \u003cem\u003eAnaerococcus\u003c/em\u003e genus [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In our study, species from the \u003cem\u003eAnaerococcus\u003c/em\u003e genus, however, were not detected. Nonetheless, current studies are hardly comparable due to the different sample size and microbiome detection methods, proving contradicting and inconclusive results. Importantly, contrastingly to our study where we analyzed species level by shotgun sequencing, the previous studies performed a 16S rRNA gene analysis, which limits a reliable taxonomic assignment to genus level.\u003c/p\u003e \u003cp\u003eRecently, a higher frequency of \u003cem\u003eFusobacterium\u003c/em\u003e in both the endometria and ovarian endometriotic tissues from 79 patients with endometriosis were detected when compared to endometria from 76 control women [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Hence, they investigated further the pathogenic role of this bacteria in the development of endometriosis. Interestingly, we detected a higher relative abundance of \u003cem\u003eFusobacterium\u003c/em\u003e sp. CAG:815 in the gut in women with endometriosis, although the differences did not remain significant after adjustment for multiple comparisons.\u003c/p\u003e \u003cp\u003eWhile evidence supporting the role of the endometrial transcriptome in endometriosis development is accumulating [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], a new debate is whether there are microbial pathways involved in the pathogenesis of the disease. In this context, our study identified several KOs possibly associated with the presence of endometriosis. We noted that a KO related to ABC transporters was enriched in women with endometriosis. Given the high regenerative capacity of the human endometrium at eutopic and ectopic sites, scientific evidence links the origin of endometriosis to stem cells [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and supports the existence of endometrial cell subpopulations as candidate endometrial stem cells based on the side population phenotype [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This characteristic is due to the differential potential of cells to efflux the Hoechst dye via the ABC family of transporter proteins expressed within the cell membrane [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The ATP-binding cassette transporter G2 (ABCG2) expression analysis in samples of endometrium from patients with and without endometriosis found that ABCG2 was highly expressed in the endothelial cells of microvessels of eutopic endometria, and reduced in those of ectopic endometria except in cases of deep infiltrating endometriosis, suggesting that ABCG2\u0026thinsp;+\u0026thinsp;microvessels may be crucial for the pathophysiology of deep infiltrating endometriosis [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Our results are in line with this hypothesis, nevertheless, further research considering the different stages of endometriosis is warranted to analyze potential alterations of the gut microbes and microbial pathways that could be hidden in early endometriosis stages.\u003c/p\u003e \u003cp\u003eAnother KO of interest in endometriosis is the long-chain saturated fatty acids biosynthesis, a metabolic pathway catalyzed by fatty acid synthase (FASN). We detected a KO related to long-chain saturated fatty acids biosynthesis more represented in women with endometriosis. In some cancer cell lines, FASN has been found to be fused with estrogen receptor, and its overexpression is a common molecular feature in hormone-sensitive cells, being regulated by both estradiol and progesterone [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. During the menstrual cycle, FASN expression appears to be linked to endometrial cell proliferation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Thus, inhibiting fatty acid synthase has been proposed as a therapy targeting estrogen receptor signaling in breast and endometrial cancer [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In fact, several studies associate the high prevalence of endometriosis with excessive lipid intake or a lipid intake imbalance and propose novel lipid metabolism-targeted approaches for the treatment of endometriosis due to the proliferative and inflammatory state of the disease [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also explored the microbial genes involved in estrogen metabolism, the estrobolome, that is recognized as an important factor in the development of proliferative disorders, including endometriosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Through a comprehensive analysis of 156 estrogen pathway-related enzymes, including main candidates like beta-glucuronidases and beta-galactosidases, no significant differences in the total read counts of these enzymes between the case-control groups were detected. Our findings suggest that alterations in the abundance of these specific enzymes from the estrobolome may not directly correlate with the presence of endometriosis in our studied cohort. Nevertheless, the estrogen-estrobolome-endometriosis axis is complex and our study results cannot rule out its importance in the disease development, which warrants further research.\u003c/p\u003e \u003cp\u003eOur study provides pioneering results about the gut microbiome composition and association with endometriosis on a large-scale study population, however, it has several limitations that should be highlighted. First, the detection power in our case-control study might have been influenced by including different subtypes of endometriosis. Endometriosis is defined as a heterogeneous disease broadly characterized into three phenotypes with different grade of severity: from superficial peritoneal as the least severe form, to ovarian and deep infiltrating endometriosis, the last being the most severe phenotype [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Since the inclusion of the three phenotypes could mask the presence of microbial alterations in the most severe forms, additional analyses on the different subtypes are needed to confirm our results. Furthermore, hormonal imbalance has been demonstrated to have a negative impact on the gut microbiome, while it has been reported that hormonal treatment reverses the gut microbiome dysbiosis in reproductive disorders [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Since the use of estrogen-progestins and progestins is the first-line medical treatment in endometriosis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], patients with hormonal treatment may present similar gut microbial profiles than those without the disease. Hence, more studies on women with active endometriosis and no hormonal treatment are warranted to unravel the complex bidirectional relationship between the gut microbiome and endometriosis.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eThe molecular mechanisms underlying the pathogenesis of endometriosis are not yet fully understood, which presents a challenge in its diagnosis and treatment. In this context, the gut microbiome emerges as a potential diagnostic tool and therapeutic target. We present the largest whole metagenome study on endometriosis so far, however our study findings do not provide enough evidence to support the existence of a gut microbiome-dependent mechanism implicated in the pathogenesis of endometriosis. Further research, especially involving large-scale study populations with active endometriosis and without hormonal treatment, is crucial to better understand the endometriosis-associated microbiome, and to unravel its potential for diagnosis and treatment approaches.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eATP-binding cassette\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANCOM-BC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Compositions of Microbiomes with Bias Correction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFASN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efatty acid synthase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKEGG Orthology group\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elinear-mixed effect models\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePartitioning Around Medoids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCoA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal coordinates analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePERMANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePermutational analysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants included in the EstMB provided informed consent for the data and samples to be used for scientific purposes. This study was approved by the Research Ethics Committee of the University of Tartu (approval No. 266/ T10) and by the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs; approval No. 1.1-12/17).\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\u003eThe metagenomic data analyzed during the current study are available in the European Genome-Phenome Archive database (https://www.ebi.ac.uk/ega/) under accession code EGAS00001008448.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by Grants Endo-Map (PID2021-12728OB-100), ROSY (CNS2022-135999) and PRE2018-085440 funded by MCIN/AEI/10.13039/501100011033 and ERFD A way of making Europe; Estonian Research Council grants (grants No. PRG1414 to EO and PRG1076);\u0026nbsp;EMBO Installation grant (No. 3573 to EO); Estonian Center of Genomics/ Roadmap II (project No. 16-0125); Horizon 2020 innovation grant (ERIN, grant No.\u0026nbsp;EU952516); Grant FPU19/05561 funded by MCIN/AEI/10.13039/501100011033 and by ESF Investing in your future; Plan de Recuperaci\u0026oacute;n, Transformaci\u0026oacute;n y resiliencia, Ayudas para la recualificaci\u0026oacute;n del sistema universitario espa\u0026ntilde;ol, Ayudas Margarita Salas (ref. UJAR01MS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.S., E.O. and S.A. conceived and designed the study; I.P.P., E.V., E.S.E., K.L., A.C.G., L.A.P., R.A., O.A., K.W., E.O. and S.A. performed the microbiome analyses; K.L., O.A., E.B. and E.O. participated in data generation; I.P.P., E.V., E.S.E., E.S.E., K.L., A.C.G., J.F., A.S., K.W., E.O. and S.A. interpreted the results. I.P.P., E.V., A.C.G. and S.A. drafted the manuscript. All authors reviewed the manuscript draft for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is part of a Ph.D. thesis conducted in the Biomedicine Doctoral Studies of the University of Granada, Spain.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTaylor HS, Kotlyar AM, Flores VA. Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet (London, England). 2021;397:839\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhine YM, Taniguchi F, Harada T. Clinical management of endometriosis-associated infertility. Reprod Med Biol. 2016;15:217\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSachedina A, Todd N. Dysmenorrhea, Endometriosis and Chronic Pelvic Pain in Adolescents. 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Front Endocrinol (Lausanne). 2021;12:794496.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Informations","content":"\u003cp\u003eSupplementary Figures and Tables are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Granada","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":"Endometriosis, estrobolome, gut microbiota, metagenomics, microbiome, microbiota, shotgun sequencing","lastPublishedDoi":"10.21203/rs.3.rs-3894655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3894655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEndometriosis, defined as the presence of endometrial-like tissue outside of the uterus, is one of the most prevalent gynecological disorders. Although different theories have been proposed, its pathogenesis is not clear. Novel studies indicate that the gut microbiome may be involved in the etiology of endometriosis, nevertheless, the connection between microbes, their dysbiosis and the development of endometriosis is understudied. This case-control study analyzed the gut microbiome in women with and without endometriosis to identify microbial targets involved in the disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA subsample of 1,000 women from the Estonian Microbiome cohort, including 136 women with endometriosis and 864 control women, was analyzed. Microbial composition was determined by shotgun metagenomics and microbial functional pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Partitioning around medoids (PAM) algorithm was performed to cluster the microbial profile of the Estonian population. The alpha- and beta-diversity and differential abundance analyses were performed to assess the gut microbiome (species and KEGG orthologies [KO]) in both groups. Metagenomic reads were mapped to estrobolome-related enzymes’ sequences to study potential microbiome-estrogen metabolism axis alterations in endometriosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiversity analyses did not detect significant differences between women with and without endometriosis (Alpha-diversity: all p-values \u0026gt; 0.05; Beta-diversity: PERMANOVA, both R\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.0007, p-values \u0026gt; 0.05). No differential species or pathways were detected after multiple testing adjustment (all FDR p-values \u0026gt; 0.05). Sensitivity analysis excluding women at menopause (\u0026gt; 50 years) confirmed our results. Estrobolome-associated enzymes’ sequences reads were not significantly different between groups (all FDR p-values \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings do not provide enough evidence to support the existence of a gut microbiome-dependent mechanism directly implicated in the pathogenesis of endometriosis. To the best of our knowledge, this is the largest metagenome study on endometriosis conducted to date.\u003c/p\u003e","manuscriptTitle":"Gut microbiome in endometriosis: a cohort study on 1,000 individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 16:03:35","doi":"10.21203/rs.3.rs-3894655/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":"d83f85f5-c74e-4ca0-ad7b-cc2954555dec","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-25T16:03:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 16:03:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3894655","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3894655","identity":"rs-3894655","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}