Predicted COVID-19 molecular effects on endometrium reveal key dysregulated genes and functions.

COVID-19 exerts systemic effects that can compromise various organs and systems. Although retrospective and in silico studies and prospective preliminary analysis have assessed the possibility of direct infection of the endometrium, there is a lack of in-depth and prospective studies on the impact of systemic disease on key endometrial genes and functions across the menstrual cycle and window of implantation. Gene expression data have been obtained from (i) healthy secretory endometrium collected from 42 women without endometrial pathologies and (ii) nasopharyngeal swabs from 231 women with COVID-19 and 30 negative controls. To predict how COVID-19-related gene expression changes impact key endometrial genes and functions, an in silico model was developed by integrating the endometrial and COVID-19 datasets in an affected mid-secretory endometrium gene co-expression network. An endometrial validation set comprising 16 women (8 confirmed to have COVID-19 and 8 negative test controls) was prospectively collected to validate the expression of key genes. We predicted that five genes important for embryo implantation were affected by COVID-19 (downregulation of COBL, GPX3 and SOCS3, and upregulation of DOCK2 and SLC2A3). We experimentally validated these genes in COVID-19 patients using endometrial biopsies during the secretory phase of the menstrual cycle. The results generally support the in silico model predictions, suggesting that the transcriptomic landscape changes mediated by COVID-19 affect endometrial receptivity genes and key processes necessary for fertility, such as immune system function, protection against oxidative damage and development vital for embryo implantation and early development.

Breaking the ageing paradigm in endometrium: endometrial gene expression related to cilia and ageing hallmarks in women over 35 years.

STUDY QUESTION: Does age affect endometrial gene expression? SUMMARY ANSWER: Using unsupervised artificial intelligence methods, we report for the first time that endometrial gene expression changes from 35 years of age in women. WHAT IS KNOWN ALREADY: Female fertility declines with age, largely attributed to declining oocyte quality and ovarian reserve. Combined with other evidence, a longstanding paradigm holds that age does not affect the endometrial function and age has not been controlled for properly in endometrial studies. STUDY DESIGN, SIZE, DURATION: A retrospective in silico analysis was performed of endometrial transcriptomic data from the Gene Expression Omnibus (GEO) sample repository for 27 women of different ages. Results were validated in an independent gene expression dataset of 20 endometrial samples from women aged 23-43 years. PARTICIPANTS/MATERIALS, SETTING, METHODS: A systematic search was performed in GEO from October 2016 to January 2019 to identify transcriptomic studies involving women of different ages. Included samples were from norm-ovulatory, women of reproductive age (23-49 years) with regular menstrual cycles who were free of endometriosis and used as controls in a previous endometrial study. We used raw gene expression data and metadata from these samples to investigate the effect of age on endometrial gene expression. Files were downloaded, pre-processed and explored for potential confounding variables and outliers. Artificial intelligence methods were applied to define age groups, and differential expression and functional analyses were applied to demonstrate and understand the effect of age on gene expression at the molecular level. Functional results were validated in an independent gene expression dataset of 20 endometrial samples from women aged 23-43 years. MAIN RESULTS AND THE ROLE OF CHANCE: Analysis of the initially retrieved endometrial datasets revealed the age of participants was not available (33.33%) or traceable (43.33%) in most studies. However, one study was suitable for age analysis (GSE4888, n = 27, 23-49 years). Samples showed different transcriptomic profiles according to age, beginning at 35 years. A total of 5778 differentially expressed genes and 27 significantly altered endometrial functions (false discovery rate (FDR) < 0.05) were associated with endometrial gene expression changes related to age. Interestingly, 81.48% of affected functions were related to up-regulation of ciliary processes, with 91 genes involved in cilia motility and ciliogenesis. Other functions included dysregulation of the vascular endothelial growth factor signalling pathway and inhibition of epithelial proliferation triggered by 37 genes involved in cell cycle arrest, angiogenesis, insulin signalling and telomere protection. These findings were validated in an independent dataset using a non-targeted approach; 20 up-regulated ciliary processes (FDR < 0.02) and 6 down-regulated functions related to cell cycle arrest were identified as affected by age, among other hallmarks of ageing such as DNA repair inhibition or sugar metabolism (FDR < 0.05). LARGE SCALE DATA: Data underlying this article are available in GEO, IDs: GSE4888 (main dataset) and GSE102131 (validation dataset). LIMITATIONS, REASONS FOR CAUTION: This study is limited in size, as are most studies of endometrial transcriptomics where whole-transcriptome analysis considers nearly 22 000 variables in a relatively small population. Yet, our study includes a main sample set and subsequent validation set that enhances reproducibility of our results and provides reasonable evidence for concluding that age affects endometrial gene expression. A larger study prospectively controlling for patient characteristics is needed to accurately describe changes related to age, with a higher sample size and across a wide age range. Additional studies also are necessary to determine the endometrial ageing contribution to infertility for ultimate translation to a clinical setting. WIDER IMPLICATIONS OF THE FINDINGS: Our findings support an influence of age on the endometrium in a genome-wide functional approach, breaking the endometrial ageing paradigm in human reproduction. To our knowledge, this work is the first to identify, using a genome-wide functional non-targeted approach, ciliary processes as the primary dysregulated function associated with maternal age. These results should guide the research community to control for age as a potential confounding variable in endometrial gene expression studies and to consider endometrial ageing in further studies as a potential cause of infertility in the clinical setting. The reported functional dysregulations could contribute to diminished embryo implantation with age and further studies will demonstrate if such dysregulation underlies some cases of implantation failure. Additionally, the discovery of these functional alterations could enable mechanistic studies, particularly around the age-related increase in uterine pathologies. STUDY FUNDING/COMPETING INTEREST(S): This research was funded by the Instituto de Salud Carlos III through Miguel Servet programme (CP20/00118) granted to Patricia Diaz-Gimeno (Spanish Government) co-funded by FEDER; and by IVI Foundation (1706-FIVI-041-PD). A.D.-P. (FPU/15/01398) and A.P.-L. (FPU18/01777) are granted by the pre-doctoral programme fellowship from the Ministry of Science, Innovation and Universities (Spanish Government). The authors do not have any competing interests to declare. TRIAL REGISTRATION NUMBER: N/A.

Identifying and optimizing human endometrial gene expression signatures for endometrial dating.

STUDY QUESTION: What are the key considerations for developing an enhanced transcriptomic method for secretory endometrial tissue dating? SUMMARY ANSWER: Multiple gene expression signature combinations can serve as biomarkers for endometrial dating, but their predictive performance is variable and depends on the number and identity of the genes included in the prediction model, the dataset characteristics and the technology employed for measuring gene expression. WHAT IS KNOWN ALREADY: Among the new generation of transcriptomic endometrial dating (TED) tools developed in the last decade, there exists variation in the technology used for measuring gene expression, the gene makeup and the prediction model design. A detailed study, comparing prediction performance across signatures for understanding signature behaviour and discrepancies in gene content between them, is lacking. STUDY DESIGN, SIZE, DURATION: A multicentre prospective study was performed between July 2018 and October 2020 at five different centres from the same group of clinics (Spain). This study recruited 281 patients and finally included in the gene expression analysis 225 Caucasian patients who underwent IVF treatment. After preprocessing and batch effect filtering, gene expression measurements from 217 patients were combined with artificial intelligence algorithms (support vector machine, random forest and k-nearest neighbours) allowing evaluation of different prediction models. In addition, secretory-phase endometrial transcriptomes from gene expression omnibus (GEO) datasets were analysed for 137 women, to study the endometrial dating capacity of genes independently and grouped by signatures. This provided data on the consistency of prediction across different gene expression technologies and datasets. PARTICIPANTS/MATERIALS, SETTING, METHODS: Endometrial biopsies were analysed using a targeted TruSeq (Illumina) custom RNA expression panel called the endometrial dating panel (ED panel). This panel included 301 genes previously considered relevant for endometrial dating as well as new genes selected for their anticipated value in detecting the secretory phase. Final samples (n = 217) were divided into a training set for signature discovery and an independent testing set for evaluation of predictive performance of the new signature. In addition, secretory-phase endometrial transcriptomes from GEO were analysed for 137 women to study endometrial dating capacity of genes independently and grouped by signatures. Predictive performance among these signatures was compared according to signature gene set size. MAIN RESULTS AND THE ROLE OF CHANCE: Testing of the ED panel allowed development of a model based on a new signature of 73 genes, which we termed 'TED' and delivers an enhanced tool for the consistent dating of the secretory phase progression, especially during the mid-secretory endometrium (3-8 days after progesterone (P) administration (P + 3-P + 8) in a hormone replacement therapy cycle). This new model showed the best predictive capacity in an independent test set for staging the endometrial tissue in the secretory phase, especially in the expected window of implantation (average of 114.5 ± 7.2 h of progesterone administered; range in our patient population of 82-172 h). Published sets of genes, in current use for endometrial dating and the new TED genes, were evaluated in parallel in whole-transcriptome datasets and in the ED panel dataset. TED signature performance was consistently excellent for all datasets assessed, frequently outperforming previously published sets of genes with a smaller number of genes for dating the endometrium in the secretory phase. Thus, this optimized set exhibited prediction consistency across datasets. LARGE SCALE DATA: The data used in this study is partially available at GEO database. GEO identifiers GSE4888, GSE29981, GSE58144, GSE98386. LIMITATIONS, REASONS FOR CAUTION: Although dating the endometrial biopsy is crucial for investigating endometrial progression and the receptivity process, further studies are needed to confirm whether or not endometrial dating methods in general are clinically useful and to guide the specific use of TED in the clinical setting. WIDER IMPLICATIONS OF THE FINDINGS: Multiple gene signature combinations provide adequate endometrial dating, but their predictive performance depends on the identity of the genes included, the gene expression platform, the algorithms used and dataset characteristics. TED is a next-generation endometrial assessment tool based on gene expression for accurate endometrial progression dating especially during the mid-secretory. STUDY FUNDING/COMPETING INTEREST(S): Research funded by IVI Foundation (1810-FIVI-066-PD). P.D.-G. visiting scientist fellowship at Oxford University (BEFPI/2010/032) and Josefa Maria Sanchez-Reyes' predoctoral fellowship (ACIF/2018/072) were supported by a program from the Generalitat Valenciana funded by the Spanish government. A.D.-P. is supported by the FPU/15/01398 predoctoral fellowship from the Ministry of Science, Innovation and Universities (Spanish Government). D.W. received support from the NIHR Oxford Biomedical Research Centre. The authors do not have any competing interests to declare.

Analysis of serum and endometrial progesterone in determining endometrial receptivity.

STUDY QUESTION: Is there a relationship between serum and endometrial progesterone (P4) levels, including P4 and metabolites (oestrone, oestradiol and 17α-hydroxyprogesterone), and endometrial receptivity? SUMMARY ANSWER: Serum P4 levels were not correlated with endometrial P4, nor associated with endometrial receptivity as determined by the ERA® test; however, endometrial P4 and 17α-hydroxyprogesterone levels were positively correlated and related to endometrial receptivity by ERA. WHAT IS KNOWN ALREADY: Acquisition of endometrial receptivity is governed by P4, which induces secretory transformation. A close relationship between serum P4 and pregnancy outcome is reported for hormone replacement therapy (HRT) cycles. However, the relationship between serum and uterine P4 levels has not been described, and it is unknown whether uterine receptivity depends more on serum or uterine P4 levels. STUDY DESIGN, SIZE, DURATION: A prospective cohort study was performed during March 2018-2019 in 85 IVF patients undergoing an evaluation-only HRT cycle with oestradiol valerate (6 mg/day) and micronised vaginal progesterone (400 mg/12 h). PARTICIPANTS/MATERIALS, SETTING, METHODS: Patients were under 50 years of age, had undergone at least one failed IVF cycle, had no uterine pathology, and had adequate endometrial thickness (> 6.5 mm). The study was conducted at IVI Valencia and IVI Foundation. An endometrial biopsy and a blood sample were collected after 5 days of P4 vaginal treatment. Measures included serum P4 levels, ERA®-based evaluation of endometrial receptivity, and endometrial P4 levels along with metabolites (oestrone, oestradiol and 17α-hydroxyprogesterone) measured by ultra-performance liquid chromatography-tandem mass spectrometry. MAIN RESULTS AND THE ROLE OF CHANCE: Seventy-nine women were included (mean age: 39.9 ± 4.6, BMI: 24.2 ± 3.9 kg/m2, endometrial thickness: 8.2 ± 1.4 mm). The percentage of endometria indicated as receptive by ERA® was 40.5%. When comparing receptive versus non-receptive groups, no differences were observed in baseline characteristics nor in steroid hormones levels in serum or endometrium. No association between serum P4 and endometrial steroid levels or ERA result was found (P < 0.05). When the population was stratified according to metabolite concentration levels, endometrial P4 and 17α-hydroxyprogesterone were significantly associated with endometrial receptivity (P < 0.05). A higher proportion of receptive endometria by ERA was observed when endometrial P4 levels were higher than 40.07 µg/ml (relative maximum) and a lower proportion of receptive endometria was associated with endometrial 17α-hydroxyprogesterone lower than 0.35 ng/ml (first quartile). A positive correlation R2 = 0.67, P < 0.001 was observed between endometrial P4 and 17α-hydroxyprogesterone levels. LIMITATIONS, REASONS FOR CAUTION: This study did not analyse pregnancy outcomes. Further, the findings can only be extrapolated to HRT cycles with micronised vaginal progesterone for luteal phase support. WIDER IMPLICATIONS OF THE FINDINGS: Our findings suggest that the combined benefits of different routes of progesterone administration for luteal phase support could be leveraged to ensure an adequate concentration of progesterone both in the uterus and in the bloodstream. Further studies will confirm whether this method can optimise both endometrial receptivity and live birth rate. Additionally, targeted treatment to increase P4 endometrial levels may normalise the timing of the window of implantation without needing to modify the progesterone administration day. STUDY FUNDING/COMPETING INTEREST(S): This research was supported by the IVI-RMA Valencia (1706-VLC-051-EL) and Consellería d'Educació, Investigació, Cultura, i esport Generalitat Valenciana (Valencian Government, Spain, GV/2018//151). Almudena Devesa-Peiro (FPU/15/01398) and Cristina Rodriguez-Varela (FPU18/01657) were supported by the FPU program fellowship from the Ministry of Science, Innovation and Universities (Spanish Government). P.D.-G. is co-inventor on the ERA patent, with non-economic benefits. The other authors have no competing interests. TRIAL REGISTRATION NUMBER: NCT03456375.

Transcriptional changes through menstrual cycle reveal a global transcriptional derepression underlying the molecular mechanism involved in the window of implantation.

The human endometrium is a dynamic tissue that only is receptive to host the embryo during a brief time in the middle secretory phase, called the window of implantation (WOI). Despite its importance, regulation of the menstrual cycle remains incompletely understood. The aim of this study was to characterize the gene cooperation and regulation of menstrual cycle progression, to dissect the molecular complexity underlying acquisition of endometrial receptivity for a successful pregnancy, and to provide the scientific community with detailed gene co-expression information throughout the menstrual cycle on a user-friendly web-tool database. A retrospective gene co-expression analysis was performed based on the endometrial receptivity array (ERarray) gene signature from 523 human endometrial samples collected across the menstrual cycle, including during the WOI. Gene co-expression analysis revealed the WOI as having the significantly smallest proportion of negative correlations for transcriptional profiles associated with successful pregnancies compared to other cycle stages, pointing to a global transcriptional derepression being involved in acquisition of endometrial receptivity. Regulation was greatest during the transition between proliferative and secretory endometrial phases. Further, we prioritized nuclear hormone receptors as major regulators of this derepression and proved that some genes and transcription factors involved in this process were dysregulated in patients with recurrent implantation failure. We also compiled the wealth of gene co-expression data to stimulate hypothesis-driven single-molecule endometrial studies in a user-friendly database: Menstrual Cycle Gene Co-expression Network (www.menstrualcyclegcn.com). This study revealed a global transcriptional repression across the menstrual cycle, which relaxes when the WOI opens for transcriptional profiles associated with successful pregnancies. These findings suggest that a global transcriptional derepression is needed for embryo implantation and early development.

Dysregulated genes and their functional pathways in luteinized granulosa cells from PCOS patients after cabergoline treatment.

Polycystic ovarian syndrome (PCOS) is a common reproductive disorder frequently associated with a substantial risk factor for ovarian hyperstimulation syndrome (OHSS). Dopamine receptor 2 (D2) agonists, like cabergoline (Cb2), have been used to reduce the OHSS risk. However, lutein granulosa cells (LGCs) from PCOS patients treated with Cb2 still show a deregulated dopaminergic tone (decreased D2 expression and low dopamine production) and increased vascularization compared to non-PCOS LGCs. Therefore, to understand the PCOS ovarian physiology, it is important to explore the mechanisms that underlie syndrome based on the therapeutic effects of Cb2. Here, LGCs from non-PCOS and PCOS patients were cultured with hCG in the absence/presence of Cb2 (n = 12). Subsequently, a transcriptomic-paired design that compared untreated vs treated LGCs within each patient was performed. After transcriptomic analysis, functions and genes were prioritized by systems biology approaches and validated by RT-qPCR. We identified that similar functions were altered in both PCOS and non-PCOS LGCs treated with Cb2; however, PCOS-treated LGCs exhibited more significant changes than non-PCOS. Among the prioritized functions, dopaminergic synapse, vascular endothelial growth factor (VEGF) signaling, apoptosis and ovarian steroidogenesis were highlighted. Finally, network modeling showed CASP9, VEGFA, AKT1, CREB, AIF, MAOA, MAPK14 and BMAL1 as key genes implicated in these pathways in Cb2 response, which might be potential biomarkers for further studies in PCOS.

Asynchronous and pathological windows of implantation: two causes of recurrent implantation failure.

STUDY QUESTION: Is endometrial recurrent implantation failure (RIF) only a matter of an asynchronous (displaced) window of implantation (WOI), or could it also be a pathological (disrupted) WOI? SUMMARY ANSWER: Our predictive results demonstrate that both displaced and disrupted WOIs exist and can present independently or together in the same RIF patient. WHAT IS KNOWN ALREADY: Since 2002, many gene expression signatures associated with endometrial receptivity and RIF have been described. Endometrial transcriptomics prediction has been applied to the human WOI in two previous studies. One study describes endometrial RIF to be the result of a temporal displacement of the WOI. The other indicates that endometrial RIF can also result from a molecularly disrupted WOI without temporal displacement. STUDY DESIGN, SIZE, DURATION: Retrospective analysis was undertaken to compare WOI endometrial transcriptomics predictions in controls (n = 72) and RIF patients (n = 43). RIF was clinically designated by the absence of implantation after four or more transfers of high quality embryos or after the placement of 10 or more embryos in multiple transfers. Endometrial tissue samples were collected from LH + 5 to LH + 8. We compared the two molecular causes of RIF to signatures currently described in the literature. We propose a new transcriptomic RIF taxonomy to fill the gap between the two hypotheses and to guide the development of clinical detection and determination of both types of RIF. PARTICIPANTS/MATERIALS, SETTING, METHODS: Utilizing 115 gene expression profiles, two different predictive designs were developed: one considering RIF versus controls removing menstrual cycle timing, called the disrupted or pathological model, and another stratifying the WOI in transcriptomic profiles related to timing for predicting displacements. The predictive value of each model was compared between all signatures selected. We propose a new genomic approach that distinguishes between both types of RIF in the same sample cohort. MAIN RESULTS AND THE ROLE OF CHANCE: From the 16 signatures analysed, we clearly predicted two causes of RIF-both a displaced WOI and an on-time but pathologically disrupted WOI. A high predictive value related to WOI profiles associated with menstrual cycle timing was found in most of the signatures. Specifically, 69% of the signatures analysed presented an accuracy higher than expected by chance in a range from 0.87 to 0.97. Displacements and disruptions were not molecularly independent, as some signatures were moderately associated with both causes. The gene and functional comparison between signatures revealed that they were not similar, although we did find functions in common and a cluster of moderate functional concordance between some of the signatures that predicted displacements (the highest Cohen's Kappa index were between 0.55 and 0.62 depending on the functional database). We propose a new transcriptomic RIF taxonomy to fill the gap between these prior studies and to establish methodology for detecting and distinguishing both types of RIF in clinical practice. Our findings indicate these two phenotypes could present independently or together in the same RIF patient. RIF patients designated by clinical criteria have been stratified transcriptomically as 18.6% with only a displaced WOI, 53.5% with a displaced and pathological WOI, 23.3% with only a disrupted WOI, and 4.7% could be a clinical RIF with non-endometrial origin. The new RIF transcriptomic taxonomy avoids menstrual cycle timing as a confounding variable that should be controlled for, distinguishing clearly between a disrupted and a displaced WOI for precision medicine in RIF. LIMITATIONS REASONS FOR CAUTION: The main objective of this study was to use transcriptomics to detect both RIF causes and to understand the role of transcriptomic signatures in these phenotypes. The predictive value in absolute terms for each signature was not indicative in these prediction designs; instead, the comparison between signatures was most important for prediction capability in the same sample cohort for both RIF causes. Clinical follow up of the RIF taxonomies proposed has not been analysed in this study, so further prospective clinical studies are necessary to determine the prevalence and penetrance of these phenotypes. WIDER IMPLICATIONS OF THE FINDINGS: The main insight from this study is a new understanding of RIF taxonomy. Understanding how to classify RIF patients to distinguish clinically between a patient who could benefit from a personalized embryo transfer day and a patient with a disrupted WOI will enable identification and stratification for the research and development of new treatments. In addition, we demonstrate that basic research designs in endometrial transcriptomics cause masking of the study variable by the menstrual cycle timing. STUDY FUNDING/COMPETING INTEREST(S): This research has been funded by IVI-RMA; the authors do not have any competing interests.