Predicting risk of endometrial failure: a biomarker signature that identifies a novel disruption independent of endometrial timing in patients undergoing hormonal replacement cycles

OBJECTIVE

To propose a new gene expression signature that identifies endometrial disruptions independent of endometrial luteal phase timing and predicts if patients are at risk of endometrial failure.

DESIGN

Multicentric, prospective study.

SETTING

Reproductive medicine research department in a public hospital affiliated with private fertility clinics and a reproductive genetics laboratory.

PATIENTS

Caucasian women (n = 281; 39.4 ± 4.8 years old with a body mass index of 22.9 ± 3.5 kg/m2) undergoing hormone replacement therapy between July 2018 and July 2021. Endometrial samples from 217 patients met RNA quality criteria for signature discovery and analysis.

INTERVENTION(S)

Endometrial biopsies collected in the mid-secretory phase.

MAIN OUTCOME MEASURE(S)

Endometrial luteal phase timing-corrected expression of 404 genes and reproductive outcomes of the first single embryo transfer (SET) after biopsy collection to identify prognostic biomarkers of endometrial failure.

RESULTS

Removal of endometrial timing variation from gene expression data allowed patients to be stratified into poor (n = 137) or good (n = 49) endometrial prognosis groups on the basis of their clinical and transcriptomic profiles. Significant differences were found between endometrial prognosis groups in terms of reproductive rates: pregnancy (44.6% vs. 79.6%), live birth (25.6% vs. 77.6%), clinical miscarriage (22.2% vs. 2.6%), and biochemical miscarriage (20.4% vs. 0%). The relative risk of endometrial failure for patients predicted as a poor endometrial prognosis was 3.3 times higher than those with a good prognosis. The differences in gene expression between both profiles were proposed as a biomarker, coined the endometrial failure risk (EFR) signature. Poor prognosis profiles were characterized by 59 upregulated and 63 downregulated genes mainly involved in regulation (17.0%), metabolism (8.4%), immune response, and inflammation (7.8%). This EFR signature had a median accuracy of 0.92 (min = 0.88, max = 0.94), median sensitivity of 0.96 (min = 0.91, max = 0.98), and median specificity of 0.84 (min = 0.77, max = 0.88), positioning itself as a promising biomarker for endometrial evaluation.

CONCLUSION(S)

The EFR signature revealed a novel endometrial disruption, independent of endometrial luteal phase timing, present in 73.7% of patients. This EFR signature stratified patients into 2 significantly distinct and clinically relevant prognosis profiles providing opportunities for personalized therapy. Nevertheless, further validations are needed before implementing this gene signature as an artificial intelligence (AI)-based tool to reduce the risk of patients experiencing endometrial failure.

Association of blood cadmium and lead levels with self-reported reproductive lifespan and pregnancy loss: The national health and nutrition examination survey 1999-2018

Cadmium and lead are known to interfere with the endocrine function. Thus, hormonally regulated processes such as menarche, menopause and pregnancy are likely influenced by chronic exposure to these metals. In US post-menopausal women, who already completed their reproductive lifespan, we evaluated the association between blood cadmium and lead levels with self-reported reproductive lifespan and personal history of pregnancy loss. We selected 5317 post-menopausal women participating in the National Health and Nutrition Examination Survey (NHANES), 1999-2018. Blood cadmium and lead levels were measured by inductively coupled plasma mass spectrometry. Reproductive lifespan was defined as the number of years between self-reported age at menarche and menopause. Personal history of pregnancy loss was defined as number of self-reported pregnancy losses out of the self-reported number of pregnancies. The fully adjusted mean difference in reproductive lifespan (95% confidence interval [CI]) comparing the 80th to the 20th percentiles of blood cadmium and lead distributions was, respectively, 0.50 (0.10, 0.91) and 0.72 (0.41, 1.03) years. Ever smoker showed stronger association of blood lead with reproductive lifespan. For self-reported pregnancy loss, the corresponding fully adjusted relative prevalence (95% CI) was 1.10 (0.93, 1.31) for cadmium and 1.10 (1.00, 1.21) for lead, and remained similar after additional adjustment for reproductive lifespan. In never smokers, the relative prevalence was 1.07 (1.04, 1.11) and 1.16 (1.05, 1.28) for blood cadmium and lead, respectively. These findings suggest that blood cadmium and lead exposures increase reproductive lifespan and prevalence of pregnancy loss in the general population. Additional studies are needed to improve the understanding of mechanisms and prevention potential of metals-related pregnancy outcomes.

Deciphering a shared transcriptomic regulation and the relative contribution of each regulator type through endometrial gene expression signatures

BACKGORUND

While various endometrial biomarkers have been characterized at the transcriptomic and functional level, there is generally a poor overlap among studies, making it unclear to what extent their upstream regulators (e.g., ovarian hormones, transcription factors (TFs) and microRNAs (miRNAs)) realistically contribute to menstrual cycle progression and function. Unmasking the intricacies of the molecular interactions in the endometrium from a novel systemic point of view will help gain a more accurate perspective of endometrial regulation and a better explanation the molecular etiology of endometrial-factor infertility.

METHODS

An in-silico analysis was carried out to identify which regulators consistently target the gene biomarkers proposed in studies related to endometrial progression and implantation failure (19 gene lists/signatures were included). The roles of these regulators, and of genes related to progesterone and estrogens, were then analysed in transcriptomic datasets compiled from samples collected throughout the menstrual cycle (n = 129), and the expression of selected TFs were prospectively validated in an independent cohort of healthy participants (n = 19).

RESULTS

A total of 3,608 distinct genes from the 19 gene lists were associated with endometrial progression and implantation failure. The lists' regulation was significantly favoured by TFs (89% (17/19) of gene lists) and progesterone (47% (8 /19) of gene lists), rather than miRNAs (5% (1/19) of gene lists) or estrogen (0% (0/19) of gene lists), respectively (FDR < 0.05). Exceptionally, two gene lists that were previously associated with implantation failure and unexplained infertility were less hormone-dependent, but primarily regulated by estrogen. Although endometrial progression genes were mainly targeted by hormones rather than non-hormonal contributors (odds ratio = 91.94, FDR < 0.05), we identified 311 TFs and 595 miRNAs not previously associated with ovarian hormones. We highlight CTCF, GATA6, hsa-miR-15a-5p, hsa-miR-218-5p, hsa-miR-107, hsa-miR-103a-3p, and hsa-miR-128-3p, as overlapping novel master regulators of endometrial function. The gene expression changes of selected regulators throughout the menstrual cycle (FDR < 0.05), dually validated in-silico and through endometrial biopsies, corroborated their potential regulatory roles in the endometrium.

CONCLUSIONS

This study revealed novel hormonal and non-hormonal regulators and their relative contributions to endometrial progression and pathology, providing new leads for the potential causes of endometrial-factor infertility.

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 has 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 COVID19 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.

P-319 Opposite functional alterations between aged endometria and that of women with uterine disorders offer plausible explanations to the increased incidence of uterine disorders with age

Study question

Which is the functional relationship between age and uterine disorders at endometrial level?

Summary answer

Ciliogenesis and cell cycle processes were oppositely altered between the endometrium of patients with uterine disorders and that of &gt; 35 y.o. women.

What is known already

Uterine disorders are complex and multifactorial conditions which incidence increases with age affecting women's reproductive health and fertility. Uterine disorders and age have been transcriptomically researched to identify potential biomarkers and underlying mechanisms in independent studies. However, there is a lack of studies comparing the effects caused by uterine disorders and age in endometrium to understand the functional relationship between them. The objective of this research was to compare the mechanisms underlying uterine disorders and age in the endometrium to understand the molecular relationship behind the increased incidence of these disorders with age.

Study design, size, duration

In silico study performed between 2016-2021 involving a systematic review at Gene Expression Omnibus sample repository to identify datasets with endometrial gene expression raw data associated to uterine disorders and age for answering the research question. Samples were preprocessed and analyzed with the same transcriptomic procedures for comparable analysis of functions affecting gene expression. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, DoRothEA and OmniPath functional databases were consulted for identifying functions/transcription factors involved.

Participants/materials, setting, methods

Nine endometrial transcriptomic datasets evaluating uterine disorders (123 cases, 127 controls) and one including control women with different ages (23–49 y.o., n = 27) were downloaded. Differentially expressed genes and gene set enrichment functional results related to uterine disorders or age for each dataset were calculated and integrated between uterine disorders under a meta-analysis with a random effects model to account for study heterogeneity. Upstream transcription factors and pathways were identified with footprinting methods.

Main results and the role of chance

All evaluated uterine disorders (adenocarcinoma (ADC), recurrent implantation failure (RIF), recurrent pregnancy loss (RPL) and eutopic endometriosis) shared a significant downregulation of six ciliary functions (FDR&lt;0.03) with 186 associated genes. Moreover, the EGFR proliferative pathway and its downstream transcription factors MYC and/or E2F, involved in cell cycle progression, were significantly upregulated in ADC, RIF and endometriosis but downregulated in RPL (all FDR&lt;0.05). Conversely, the endometrium of women &gt;35 y.o. presented an opposite functional profile in comparison with the effect of uterine disorders - with a significant upregulation of 22 ciliary functions (FDR&lt;0.03) and a downregulation of epithelial cell proliferation (FDR=0.014) - with the exception of RPL which also presented a significant downregulation of the cell cycle (FDR&lt;0.05). We identified 91 significantly upregulated genes as major contributors of cilia alterations with age (49.5% of them being shared with the 186 cilia-related genes detected in uterine disorders); and 37 significantly downregulated cell cycle genes, including EGFR (all FDR&lt;0.05). Since ciliogenesis and cell cycle progression present an antagonistic relationship, upregulation of ciliary functions could be related to the downregulation of cell cycle processes and viceversa.

Limitations, reasons for caution

This study depends on publicly available datasets to analyze. Although we considered potential confounding variables (time of biopsy collection, presence of benign pathologies in aged women) and study heterogeneity (using random effects models accounting for study variability), further studies are needed to corroborate our findings and test the proposed hypothesis.

Wider implications of the findings

A new hypothesis was generated regarding the molecular mechanisms behind the increased incidence of uterine disorders with age: With aging, the endometrium exhibits cell cycle arrest, inhibiting ciliogenesis. Consequently, compensatory mechanisms are activated to counteract aging-related alterations, but these mechanisms could be unbalanced towards the other extreme, originating distinct disorders.

Trial registration number

Not applicable

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.

Pathways and factors regulated by bone marrow-derived stem cells in human ovarian tissue

OBJECTIVE

To describe molecular and paracrine signaling changes produced by human bone marrow-derived stem cells (BMDSC) in human ovarian cortex.

DESIGN

Experimental study.

SETTING

University hospital research laboratories.

PATIENT(S)

Ovarian cortex from poor responder women (n = 7).

ANIMALS

Immunodeficient NOD/SCID female mice (n = 18).

INTERVENTION(S)

Human ovarian cortex strips were xenografted into ovariectomized NOD/SCID female mice. A week later, mice were infused with phosphate-buffered saline, 1 × 106 BMDSC, or 3 × 105 CD133+ cells via tail vein. Gene expression changes and enriched pathways were assessed by RT2 Profiler Arrays. Several upregulated genes were validated in individual samples by real-time quantitative PCR, and transcriptomic results were reinforced by a proteomic assessment.

MAIN OUTCOME MEASURE(S)

Gene expression changes, enriched Kyoto Encyclopedia of Genes and Genomes pathways, and paracrine factors.

RESULT(S)

Seventy-four Kyoto Encyclopedia of Genes and Genomes pathways were upregulated, with the PI3K-Akt signaling pathway the most enriched after BMDSC and CD133 treatments. The greatest transcriptomic changes were seen on day 14 in the BMDSC group, affecting the regulation of paracrine factors such as KITLG, THBS1, SERPINF1, and TIMP2. Proteomics data verified changes in FoxO signaling, actin cytoskeleton remodeling, and apoptosis by BMDSC.

CONCLUSION(S)

We identified paracrine factors and pathways regulated by BMDSC that may be future targets of treatment for the increasing number of poor responder women. Our findings suggest that BMDSC upregulated soluble factors such as KITLG, THBS1, SERPINF1, and TIMP2 as well as PI3K-Akt signaling and regulation of actin cytoskeleton pathways. The identification of these putative underlying mechanisms informs future experiments aiming to optimizing clinical application of BMDSC.

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.

P–294 Mapping COVID–19 affected genes from blood in a Window of implantation co-expression network reveals a potentially compromised landscape

Study question

Could the transcriptomic and functional landscape of the window of implantation be compromised by SARS-COV–2 infection?

Summary answer

Some of the main genes and pathways involved in the window of implantation are affected in blood of COVID–19 patients and receptivity could be affected.

What is known already

There is a concern whether SARS-COV–2 can disrupt assisted reproduction treatments (ARTs) and fertility in short and long terms. In the endometrium, it was found that genes related to the viral infection (ACE2, TMPRSS2/4, CTSL/B) are involved in menstrual cycle progression, especially in the Window of Implantation (WOI). However, there are no studies describing the transcriptome changes after the infection, and the changes that could affect receptivity and embryo implantation. Currently transcriptomic datasets are publicly available regarding virus infection effects in blood. The aim of this study was to integrate these blood effects with the gene expression during the WOI.

Study design, size, duration

A public dataset with blood transcriptome of 231 female COVID–19 patients and 30 female controls was downloaded from GEO. Meanwhile, 5 transcriptomic endometrial datasets in the WOI with patients without endometrial pathologies were also retrieved (n = 44). Gene expression correlations (potential activations and inhibitions) were calculated in endometrium and filtered by blood differentially expressed genes for predicting the potential effects of COVID–19 in endometrial factor. Additionally, we discovered new endometrial genes involved in the infection repercussions.

Participants/materials, setting, methods

A gene co-expression network was built in Cytoscape with the WOI dataset [Pearson correlation = 0.65, only significant correlations; Power fit law R2 > = 0.8]. Differential expression was done for COVID–19 patients versus controls with limma and significant genes in blood were highlighted in the endometrial WOI network. Topological parameters were calculated by CytoHubba and network modules and related functions were analysed performing a Functional enrichment (BINGO). Statistical significance cut off was stablished in FDR<0.05.

Main results and the role of chance

After filtering by blood affected genes, 2051 genes were found differentially expressed in COVID–19 females in blood and mapped in the co-expression WOI network. Nine modules were highlighted being enriched in translational elongation, intracellular protein transport, endosome organization, vitamin D receptor binding, actin cytoskeleton organization, RNA splicing, among others. Important hubs in the endometrium that correlated with TMPRSS4 were: COBL, a gene that promotes formation of cell ruffles which are important or embryo adhesion (FC = –3.99, degree = 209); PKP2 (FC = –1.5, degree = 188) which could play a role in junctional plaques and knockdown in mice was reported to inhibit implantation; SOCS3, linked to unexplained infertility and pregnancy loss, (FC –4.3, degree = 177); GPX3 involved in detoxification and usually highly upregulated during WOI was downregulated (FC –3.7, degree = 173). GPX3 also correlated with CTSB. TPRC/CD45, related to unexplained pregnancy loss and concentration of NK cells, was an upregulated gene (FC = 5, degree = 161) that correlated with CTSL. Upregulated genes with main connections in the network were: SERPING1 (FC = 5), which regulates complement activation and embryo-maternal immune modulation and SMARCAD1 (FC 1.5), involved in DNA repair and heterochromatin organization.

Limitations, reasons for caution

This is an in-silico descriptive study where differentially expressed genes in blood samples of COVID–19 patients were analysed in an endometrial co-expression network context. Studying a COVID–19 infected endometrium during WOI would help to confirm the results of this study.

Wider implications of the findings: Although ACE2 has been reported as not highly expressed during the WOI, this study describes potential genes and functions very important for embryo implantation affected after SARS-COV–2 infection. These findings evidenced how SARS-COV–2 could impact the efficacy of ARTs and should be taken into consideration for further research and implications.

Trial registration number

Not applicable

Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure

Ovarian failure (OF) is a common cause of infertility usually diagnosed as idiopathic, with genetic causes accounting for 10-25% of cases. Whole-exome sequencing (WES) may enable identifying contributing genes and variant profiles to stratify the population into subtypes of OF. This study sought to identify a blood-based gene variant profile using accumulation of rare variants to promote precision medicine in fertility preservation programs. A case-control (n = 118, n = 32, respectively) WES study was performed in which only non-synonymous rare variants <5% minor allele frequency (MAF; in the IGSR) and coverage ≥ 100× were considered. A profile of 66 variants of uncertain significance was used for training an unsupervised machine learning model to separate cases from controls (97.2% sensitivity, 99.2% specificity) and stratify the population into two subtypes of OF (A and B) (93.31% sensitivity, 96.67% specificity). Model testing within the IGSR female population predicted 0.5% of women as subtype A and 2.4% as subtype B. This is the first study linking OF to the accumulation of rare variants and generates a new potential taxonomy supporting application of this approach for precision medicine in fertility preservation.

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.

Guidelines for biomarker discovery in endometrium: correcting for menstrual cycle bias reveals new genes associated with uterine disorders

Transcriptomic approaches are increasingly used in reproductive medicine to identify candidate endometrial biomarkers. However, it is known that endometrial progression in the molecular biology of the menstrual cycle is a main factor that could affect the discovery of disorder-related genes. Therefore, the aim of this study was to systematically review current practices for considering the menstrual cycle effect and to demonstrate its bias in the identification of potential biomarkers. From the 35 studies meeting the criteria, 31.43% did not register the menstrual cycle phase. We analysed the menstrual cycle effect in 11 papers (including 12 studies) from Gene Expression Omnibus: three evaluating endometriosis, two evaluating recurrent implantation failure, one evaluating recurrent pregnancy loss, one evaluating uterine fibroids and five control studies, which collected endometrial samples throughout menstrual cycle. An average of 44.2% more genes were identified after removing menstrual cycle bias using linear models. This effect was observed even if studies were balanced in the proportion of samples collected at different endometrial stages or only in the mid-secretory phase. Our bias correction method increased the statistical power by retrieving more candidate genes than per-phase independent analyses. Thanks to this practice, we discovered 544 novel candidate genes for eutopic endometriosis, 158 genes for ectopic ovarian endometriosis and 27 genes for recurrent implantation failure. In conclusion, we demonstrate that menstrual cycle progression masks molecular biomarkers, provides new guidelines to unmask them and proposes a new classification that distinguishes between biomarkers of disorder or/and menstrual cycle progression.

STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.

Uterine disorders affecting female fertility: what are the molecular functions altered in endometrium?

OBJECTIVE

To determine the molecular functions of genes exhibiting altered expression in the endometrium of women with uterine disorders affecting fertility.

DESIGN

Retrospective analysis integrating case and control data from multiple cohorts with endometrium gene expression in women with uterine disorders.

SETTING

Infertility research department affiliated with a university hospital.

PATIENT(S)

Two hundred and forty women, 121 of whom were controls, 119 of whom had endometrial adenocarcinoma (ADC), recurrent implantation failure (RIF), recurrent pregnancy loss (RPL), or stage II-IV endometriosis.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Genomewide gene expression and altered molecular functions in the endometrium of each uterine disorder.

RESULT(S)

Using robust analysis methods, we identified statistically significantly altered endometrial functions in all the uterine disorders. Cell cycle alterations were shared among all the pathologies investigated. Endometriosis was characterized by the down-regulation of ciliary processes. Among the endometriosis, ADC, and RIF samples, mitochondrial dysfunction and protein degradation were shared dysregulated processes. In addition, RPL had the most distinct functional profile, and 95% of affected functions were down-regulated.

CONCLUSION(S)

The most robust functions dysregulated in the endometrium of patients with uterine disorders across sample cohorts implicated an endometrial factor at the gene expression level. This shared endometrial factor affects endometrial receptivity processes.

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.

Does an increased body mass index affect endometrial gene expression patterns in infertile patients? A functional genomics analysis

OBJECTIVE

To analyze the transcriptomic profile of endometrial gene alterations during the window of implantation in infertile obese patients.

DESIGN

Multicenter, prospective, case-control study.

SETTING

Three academic medical centers for reproductive medicine.

PATIENT(S)

Infertile patients, stratified into body mass index (BMI) categories according to the World Health Organization guidelines, were included in the study.

INTERVENTION(S)

Endometrial samples were obtained from women undergoing standardized estrogen and P replacement cycles after 5 days of vaginal P supplementation.

MAIN OUTCOME MEASURE(S)

To identify endometrial gene expression alterations that occur during the window of implantation in infertile obese patients as compared with infertile normal-weight controls using a microarray analysis.

RESULT(S)

XCL1, XCL2, HMHA1, S100A1, KLRC1, COTL1, COL16A1, KRT7, and MFAP5 are significantly dysregulated during the window of implantation in the receptive endometrium of obese patients. COL16A1, COTL1, HMHA1, KRCL1, XCL1, and XCL2 were down-regulated and KRT7, MFAP5, and S100A1 were up-regulated in the endometrium of obese patients. These genes are mainly involved in chemokine, cytokine, and immune system activity and in the structural extracellular matrix and protein-binding molecular functions.

CONCLUSION(S)

Obesity is associated with significant endometrial transcriptomic differences as compared with non-obese subjects. Altered endometrial gene expression in obese patients may contribute to the lower implantation rates and increased miscarriage rates seen in obese infertile patients.

CLINICAL TRIAL REGISTRATION NUMBER

NCT02205866.

Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity

Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).