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Endometrial Microbiota is More Diverse in Women with Endometriosis than Symptomatic Controls

ABSTRACT: Endometriosis is a chronic, estrogen-dependent gynecological condition that affects approximately 10% of women of reproductive age. The most widely accepted theory of the etiology of endometriosis includes the process of retrograde menstruation, where menstrual effluent travels up the Fallopian tubes, accesses the peritoneal cavity, and in some people is able to establish endometriotic lesions. Recent reports suggest the uterus is not devoid of bacteria, as was once believed. Thus, the refluxed menstrual effluent may also carry bacteria along with it, and this bacteria has been suggested to contribute to inflammation, and establishment and growth of endometriotic lesions. Here, we sought to compare and contrast the uterine bacteria (endometrial microbiota) in women with surgically confirmed presence or absence of endometriosis using next-generation 16S rRNA gene sequencing. We obtained an average of more than 9000 sequence reads per endometrial biopsy, and found that the endometrial microbiota of women with endometriosis was more diverse (greater Shannon Diversity Index and greater proportion of ‘Other’ taxa) than that of symptomatic controls (women with pelvic pain, but with surgically confirmed absence of endometriosis; diagnosed with other benign gynecological conditions at surgery). The difference in endometrial microbiotas was supported in unsupervised cluster analyses where some clustering of endometrial microbiota by disease status (endometriosis vs. controls) was observed. The bacterial taxa enriched in the endometrial microbiota of women with endometriosis belonged to the Actinobacteria phylum, Oxalobacteraceae and Streptococcaceae families, and Tepidimonas genus, while those enriched in the symptomatic controls (without endometriosis) belonged to the Burkholderiaceae family, and Ralstonia genus. Taken together, our findings suggest the endometrial microbiota is perturbed in people with endometriosis.

ORGANISM(S): Homo sapiens

PROVIDER: GSE172172 | GEO | 2021/04/16

REPOSITORIES: GEO

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Project description:Endometriosis is associated with aberrant gene expression in the eutopic endometrium of women with disease. To determine if the development of endometriotic lesions directly impacts eutopic endometrial gene expression, we sequentially analyzed the eutopic endometrium across the time course of disease progression in a baboon model of induced disease. Endometriosis was induced in baboons by intraperitoneal inoculation of autologous menstrual endometrium. Eutopic endometria were collected at 9-11 days postovulation) in five time points: 1, 3, 6-7, 10-12, and 15-16 months after disease induction and compared with tissue from disease-free baboons. We used microarrays to identify differentially expressed genes between time points. Sequential analysis of the same animals during disease progression demonstrated an early disease insult and a transitory dominance of an estrogenic phenotype. However, as the disease progressed, a progesterone-resistant phenotype became evident. Endometriosis was experimentally induced in Papio anubis female baboons (n = 4) by intraperitoneal inoculation with menstrual endometrium on two consecutive menstrual cycles. Baboons with spontaneous endometriosis (n = 2) were also included in this study with an unknown duration of disease. Control endometrium was similarly harvested from animals (n = 4) with no previous surgeries and with no visible disease. The progression of disease was monitored in each animal by consecutive laparoscopies and video recording at 1 (n = 2), 3 (n = 4), 6-7 (n = 4), 10-12, (n = 4), and 15-16 (n = 3) months after inoculation during a window of 9-11 days postovulation. Eutopic endometrial tissues were harvested and were snap frozen in liquid nitrogen for RNA extraction. Additionally, menstrual endometrium was harvested on Days 1-2 of menses using a Unimar Pipelle (Cooper Surgical Inc., Shelton, CT) immediately prior to laparoscopy. Blood samples were collected daily from days 7 through 16 postmenstruation of menstrual cycles.

Project description:Endometriosis is a debilitating gynecological disorder affecting approximately 10% of the female population. Despite its prevalence, robust methods to classify and treat endometriosis remain elusive. Changes throughout the menstrual cycle in tissue size, architecture, cellular composition, and individual cell phenotypes make it extraordinarily challenging to identify markers or cell types associated with uterine pathologies since disease-state alterations in gene and protein expression are convoluted with cycle phase variations. Here, we developed an integrated workflow to generate both proteomic and single-cell RNA-sequencing (scRNA-seq) data sets using tissues and cells isolated from the uteri of control and endometriotic donors. Using a linear mixed effect model (LMM), we identified proteins associated with cycle stage and disease, revealing a set of genes that drive separation across these two biological variables. Further, we analyzed our scRNA-seq data to identify cell types expressing cycle and disease- associated genes identified in our proteomic data. A module scoring approach was used to identify cell types driving the enrichment of certain biological pathways, revealing several pathways of interest across different cell subpopulations. Finally, we identified ligand-receptor pairs including Axl/Tyro3 – Gas6, that may modulate interactions between endometrial macrophages and/or endometrial stromal/epithelial cells. Analysis of these signaling pathways in an independent cohort of endometrial biopsies revealed a significant decrease in Tyro3 expression in patients with endometriosis compared to controls, both transcriptionally and through histological staining. This measured decrease in Tryo3 in patients with disease could serve as a novel diagnostic biomarker or treatment avenue for patients with endometriosis. Taken together, this integrated approach provides a framework for integrating LMMs, proteomic and RNA-seq data to deconvolve the complexities of complex uterine diseases and identify novel genes and pathways underlying endometriosis.

Project description:Endometriosis, an estrogen-dependent, progesterone-resistant, inflammatory disorder affects 10% of reproductive-age women. It is diagnosed and staged at surgery, resulting in an 11-year latency from symptom onset to diagnosis, underscoring the need for less invasive, less expensive approaches. Since the uterine lining (endometrium) in women with endometriosis has altered molecular profiles, we tested whether molecular classification of this tissue can distinguish and stage disease. We developed classifiers using genomic data from n=148 archived endometrial samples from women with endometriosis or without endometriosis (normal controls or with other common uterine/pelvic pathologies) across the menstrual cycle and evaluated their performance on independent sample sets. Classifiers were trained separately on samples in specific hormonal milieu, using margin tree classification, and accuracies were scored on independent validation samples. Classification of samples from women with endometriosis or no endometriosis involved two binary decisions each based on expression of specific genes. These first distinguished presence or absence of uterine/pelvic pathology and then no endometriosis from endometriosis, with the latter further classified according to severity (minimal/mild or moderate/severe). Best performing classifiers identified endometriosis with 90-100% accuracy, were cycle phase-specific or independent, and utilized relatively few genes to determine disease and severity. Differential gene expression and pathway analyses revealed immune activation, altered steroid and thyroid hormone signaling/metabolism and growth factor signaling in endometrium of women with endometriosis. Similar findings were observed with other disorders versus controls. Thus, classifier analysis of genomic data from endometrium can detect and stage pelvic endometriosis with high accuracy, dependent or independent of hormonal milieu. We propose that limited classifier candidate-genes are of high value in developing diagnostics and identifying therapeutic targets. Discovery of endometrial molecular differences in the presence of endometriosis and other uterine/pelvic pathologies raises the broader biological question of their impact on the steroid hormone response and normal functions of this tissue. We analyzed endometrial samples from n=148 women without or with endometriosis and/or other uterine/pelvic pathologies, using whole genome microarrays.

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Endometrial Microbiota is More Diverse in Women with Endometriosis than Symptomatic Controls

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