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.

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.

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.

Endometrial gene expression differences in women with coronavirus disease 2019.

OBJECTIVE: To study the potential effect of coronavirus disease (COVID-19) on the endometrial transcriptome of affected, symptomatic women for the detection of altered gene expression. DESIGN: Pilot study of the endometrial transcriptomes of women manifesting COVID-19 compared with those of women without COVID-19 undergoing hysteroscopic procedures for benign gynecologic disorders using RNA sequencing. SETTING: Hospital and university laboratories. PATIENT(S): Women with (n = 14) and without a COVID-19 (n = 10) diagnosis based on a nasopharyngeal swab analysis using quantitative reverse-transcription polymerase chain reaction. The endometrium of the patients with COVID-19 had previously been tested for severe acute respiratory syndrome coronavirus 2 infection, revealing the absence of the virus in this tissue. INTERVENTION(S): Endometrial biopsy sample collection. MAIN OUTCOMES MEASURE(S): Endometrial gene expression and functional analysis of symptomatic patients with COVID-19 vs. individuals without the infection. RESULT(S): The systemic disease COVID-19 altered endometrial gene expression in 75% of the women, with the patients exhibiting a preponderance of 163 up-regulated (e.g., UTS2, IFI6, IFIH1, and BNIP3) and 72 down-regulated genes (e.g., CPZ, CDH3, and IRF4) (false discovery rate<0.05). A total of 161 dysregulated functions (36 up-regulated and 125 down-regulated) were typically enriched in the endometria of the patients with COVID-19, including up-regulation in pathways involved in the development of immune responses to viruses and cytokine inflammation, reflecting elicitation of a COVID-19 response pathway. CONCLUSION(S): Coronavirus disease 2019 affects endometrial gene expression despite the absence of severe acute respiratory syndrome coronavirus 2 RNA in endometrial tissues.

Global Methylome Scores Correlate with Histological Subtypes of Colorectal Carcinoma and Show Different Associations with Common Clinical and Molecular Features.

BACKGROUND: The typical methylation patterns associated with cancer are hypermethylation at gene promoters and global genome hypomethylation. Aberrant CpG island hypermethylation at promoter regions and global genome hypomethylation have not been associated with histological colorectal carcinomas (CRC) subsets. Using Illumina's 450 k Infinium Human Methylation beadchip, the methylome of 82 CRCs were analyzed, comprising different histological subtypes: 40 serrated adenocarcinomas (SAC), 32 conventional carcinomas (CC) and 10 CRCs showing histological and molecular features of microsatellite instability (hmMSI-H), and, additionally, 35 normal adjacent mucosae. Scores reflecting the overall methylation at 250 bp, 1 kb and 2 kb from the transcription starting site (TSS) were studied. RESULTS: SAC has an intermediate methylation pattern between CC and hmMSI-H for the three genome locations. In addition, the shift from promoter hypermethylation to genomic hypomethylation occurs at a small sequence between 250 bp and 1 Kb from the gene TSS, and an asymmetric distribution of methylation was observed between both sides of the CpG islands (N vs. S shores). CONCLUSION: These findings show that different histological subtypes of CRC have a particular global methylation pattern depending on sequence distance to TSS and highlight the so far underestimated importance of CpGs aberrantly hypomethylated in the clinical phenotype of CRCs.

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.

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.

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.

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.

SARS-CoV-2 infection risk assessment in the endometrium: viral infection-related gene expression across the menstrual cycle.

OBJECTIVE: To determine the susceptibility of the endometrium to infection by-and thereby potential damage from-SARS-CoV-2. DESIGN: Analysis of SARS-Cov-2 infection-related gene expression from endometrial transcriptomic data sets. SETTING: Infertility research department affiliated with a public hospital. PATIENT(S): Gene expression data from five studies in 112 patients with normal endometrium collected throughout the menstrual cycle. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Gene expression and correlation between viral infectivity genes and age throughout the menstrual cycle. RESULT(S): Gene expression was high for TMPRSS4, CTSL, CTSB, FURIN, MX1, and BSG; medium for TMPRSS2; and low for ACE2. ACE2, TMPRSS4, CTSB, CTSL, and MX1 expression increased toward the window of implantation. TMPRSS4 expression was positively correlated with ACE2, CTSB, CTSL, MX1, and FURIN during several cycle phases; TMPRSS2 was not statistically significantly altered across the cycle. ACE2, TMPRSS4, CTSB, CTSL, BSG, and MX1 expression increased with age, especially in early phases of the cycle. CONCLUSION(S): Endometrial tissue is likely safe from SARS-CoV-2 cell entry based on ACE2 and TMPRSS2 expression, but susceptibility increases with age. Further, TMPRSS4, along with BSG-mediated viral entry into cells, could imply a susceptible environment for SARS-CoV-2 entry via different mechanisms. Additional studies are warranted to determine the true risk of endometrial infection by SARS-CoV-2 and implications for fertility treatments.

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.

iTRAQ comparison of proteomic profiles of endometrial receptivity.

Endometrial receptivity is a limiting step in human reproduction. A disruption in the development of endometrial receptivity is responsible for recurrent implantation failures (RIF) of endometrial origin. To understand the molecular mechanisms behind the endometrial receptivity process, we used the isobaric tag for relative and absolute quantitation (iTRAQ) method to compare three different endometrial statuses: fertile women, intrauterine device (IUD) carriers, and RIF patients. Overall, iTRAQ allowed identified 1889 non-redundant proteins. Of these, 188 were differentially expressed proteins (DEP) (p-value < .05). Pairwise comparisons revealed 133 significant DEP in fertile vs. IUD carriers and 158 DEP in RIF vs. IUD carriers. However, no DEP were identified between fertile and RIF patients. Western blot validation of three DEP involved in endometrial receptivity (plastin 2, lactotransferrin, and lysozyme) confirmed our iTRAQ results. Moreover, functional KEGG enrichment revealed that complement and coagulation cascades and peroxisome were the two most significant pathways for the RIF vs. IUD comparison and ribosome and spliceosome for the fertile vs. IUD comparison, as possible important pathways involved in the endometrial receptivity acquisition. The lack of DEP between fertile and RIF patient endometria suggest that idiopathic RIF may not have an endometrial origin, with other as-yet-unknown factors involved. SIGNIFICANCE: A pilot study where a comparison of the endometrial protein profile from women with different endometrial receptive grade (fertile women, IUD carriers and RIF patients) during the same period of time (overlapping with the window of implantation) of a hormone replacement therapy was performed using a high-throughput proteomic technique. This approach lead us to better understand the molecular mechanisms undergoing endometrial receptivity, a time-limiting step to achieve pregnancy in humans. Moreover, the number of samples per group (10 Fertile women, 10 IUD carriers and 8 RIF patients) according to the methodology here employed (8plex iTRAQ), give more robustness to our results. Our findings confirm that an IUD introduces numerous changes in the endometrial protein profile when compared to fertile and RIF endometria, revealing some key proteins involved in endometrial receptivity. Finding no significant differences between Fertile and RIF patient endometria could suggest that other as-yet-unknown factors could be involved in the etiology of idiopathic RIF.

Transcriptomic behavior of genes associated with chromosome 21 aneuploidies in early embryo development.

OBJECTIVE: To analyze how chromosome 21 (HSA21) ploidy affects global gene expression of early human blastocysts. DESIGN: Prospective study. SETTING: University-affiliated in vitro fertilization clinic. PATIENT(S): A total of 26 high-quality donated embryos from in vitro fertilization (IVF) patients: trisomy 21 (n = 8), monosomy 21 (n = 10), and euploid (n = 8) blastocysts. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Blastocyst transcriptome changes and its associated functions. RESULT(S): Trisomy 21, monosomy 21, and euploid blastocysts were classified by comparative genomic hybridization. The global transcriptome of whole blastocysts was analyzed with small cell number RNA sequencing, and they were compared to understand the gene expression behavior at early development and its implications for embryo implantation. We identified 1,232 differentially expressed genes (false discovery rate <0.05) in monosomy 21 compared with euploid blastocysts associated with dysregulated functions in embryo development as the Rap1 signaling pathway. Curiously, Down syndrome in early development revealed fewer transcriptomic changes than expected. In addition, Down syndrome gene expression in neonates, children, and adults revealed that the number of deregulated genes increases across life stages from blastocysts to adults, suggesting a potential dosage-compensation mechanism for human chromosome 21. CONCLUSION(S): At the transcriptomic level, early development in Down syndrome is mainly dosage compensated. However, monosomy 21 is strongly transcriptionally affected because early development involving main functions is associated with embryo implantation.

Window of implantation transcriptomic stratification reveals different endometrial subsignatures associated with live birth and biochemical pregnancy.

OBJECTIVE: To refine the endometrial window of implantation (WOI) transcriptomic signature by defining new subsignatures associated to live birth and biochemical pregnancy. DESIGN: Retrospective cohort study. SETTING: University-affiliated in vitro fertilization clinic and reproductive genetics laboratory. PATIENT(S): Healthy fertile oocyte donors (n = 79) and patients with infertility diagnosed by Endometrial Receptivity Analysis (n = 771). INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): WOI transcriptomic signatures associated with specific reproductive outcomes. RESULT(S): The retrospective cohort study was designed to perform a prediction model based on transcriptomic clusters for endometrial classification (training set, n = 529). The clinical follow-up set in the expected WOI (n = 321) was tested with the transcriptomic predictor to detect WOI variability and the pregnancy outcomes associated with these subsignatures (n = 228). The endometrial receptivity signature was redefined into four WOI transcriptomic profiles. This stratification identified an optimal endometrial receptivity (RR) signature resulting in an ongoing pregnancy rate (OPR) of 80% in terms of live birth, as well as a late receptive-stage (LR) signature with a potential high risk of 50% biochemical pregnancy. Abnormal down-regulation of the cell cycle was the main dysregulated function among the 22 genes associated with biochemical pregnancy. CONCLUSION(S): The major differences between the WOI transcriptomic stratification were in the OPR and biochemical pregnancy rate. The OPR ranged from 76.9% and 80% in the late prereceptive (LPR) and RR signatures, respectively, versus 33.3% in the LR. The biochemical pregnancy rate was 7.7% and 6.6% in LPR and RR, respectively, but 50% in LR, which highlights the relevance of endometrial status in the progression of embryonic implantation.

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.

Separating common from distinctive variation.

BACKGROUND: Joint and individual variation explained (JIVE), distinct and common simultaneous component analysis (DISCO) and O2-PLS, a two-block (X-Y) latent variable regression method with an integral OSC filter can all be used for the integrated analysis of multiple data sets and decompose them in three terms: a low(er)-rank approximation capturing common variation across data sets, low(er)-rank approximations for structured variation distinctive for each data set, and residual noise. In this paper these three methods are compared with respect to their mathematical properties and their respective ways of defining common and distinctive variation. RESULTS: The methods are all applied on simulated data and mRNA and miRNA data-sets from GlioBlastoma Multiform (GBM) brain tumors to examine their overlap and differences. When the common variation is abundant, all methods are able to find the correct solution. With real data however, complexities in the data are treated differently by the three methods. CONCLUSIONS: All three methods have their own approach to estimate common and distinctive variation with their specific strength and weaknesses. Due to their orthogonality properties and their used algorithms their view on the data is slightly different. By assuming orthogonality between common and distinctive, true natural or biological phenomena that may not be orthogonal at all might be misinterpreted.

Chronic subordination stress selectively downregulates the insulin signaling pathway in liver and skeletal muscle but not in adipose tissue of male mice.

Chronic stress has been associated with obesity, glucose intolerance, and insulin resistance. We developed a model of chronic psychosocial stress (CPS) in which subordinate mice are vulnerable to obesity and the metabolic-like syndrome while dominant mice exhibit a healthy metabolic phenotype. Here we tested the hypothesis that the metabolic difference between subordinate and dominant mice is associated with changes in functional pathways relevant for insulin sensitivity, glucose and lipid homeostasis. Male mice were exposed to CPS for four weeks and fed either a standard diet or a high-fat diet (HFD). We first measured, by real-time PCR candidate genes, in the liver, skeletal muscle, and the perigonadal white adipose tissue (pWAT). Subsequently, we used a probabilistic analysis approach to analyze different ways in which signals can be transmitted across the pathways in each tissue. Results showed that subordinate mice displayed a drastic downregulation of the insulin pathway in liver and muscle, indicative of insulin resistance, already on standard diet. Conversely, pWAT showed molecular changes suggestive of facilitated fat deposition in an otherwise insulin-sensitive tissue. The molecular changes in subordinate mice fed a standard diet were greater compared to HFD-fed controls. Finally, dominant mice maintained a substantially normal metabolic and molecular phenotype even when fed a HFD. Overall, our data demonstrate that subordination stress is a potent stimulus for the downregulation of the insulin signaling pathway in liver and muscle and a major risk factor for the development of obesity, insulin resistance, and type 2 diabetes mellitus.

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).

Methylome profiling reveals functions and genes which are differentially methylated in serrated compared to conventional colorectal carcinoma.

BACKGROUND: Serrated adenocarcinoma (SAC) is a recently recognized colorectal cancer (CRC) subtype accounting for 7.5-8.7 % of CRCs. It has been shown that SAC has a worse prognosis and different histological and molecular features compared to conventional carcinoma (CC) but, to date, there is no study analysing its methylome profile. RESULTS: The methylation status of 450,000 CpG sites using the Infinium Human Methylation 450 BeadChip array was investigated in 103 colorectal specimens, including 39 SACs and 34 matched CCs, from Spanish and Finnish patients. Microarray data showed a higher representation of morphogenesis-, neurogenesis-, cytoskeleton- and vesicle transport-related functions and also significant differential methylation of 15 genes, including the iodothyronine deiodinase DIO3 and the forkhead family transcription factor FOXD2 genes which were validated at the CpG, mRNA and protein level using pyrosequencing, methylation-specific PCR, quantitative polymerase chain reaction (qPCR) and immunohistochemistry. A quantification study of the methylation status of CpG sequences in FOXD2 demonstrated a novel region controlling gene expression. Moreover, differences in these markers were also evident when comparing SAC with CRC showing molecular and histological features of high-level microsatellite instability. CONCLUSIONS: This methylome study demonstrates distinct epigenetic regulation patterns in SAC which are consistent to previous expression profile studies and that DIO3 and FOXD2 might be molecular targets for a specific histology-oriented treatment of CRC.

Assessing the impact of mutations found in next generation sequencing data over human signaling pathways.

Modern sequencing technologies produce increasingly detailed data on genomic variation. However, conventional methods for relating either individual variants or mutated genes to phenotypes present known limitations given the complex, multigenic nature of many diseases or traits. Here we present PATHiVar, a web-based tool that integrates genomic variation data with gene expression tissue information. PATHiVar constitutes a new generation of genomic data analysis methods that allow studying variants found in next generation sequencing experiment in the context of signaling pathways. Simple Boolean models of pathways provide detailed descriptions of the impact of mutations in cell functionality so as, recurrences in functionality failures can easily be related to diseases, even if they are produced by mutations in different genes. Patterns of changes in signal transmission circuits, often unpredictable from individual genes mutated, correspond to patterns of affected functionalities that can be related to complex traits such as disease progression, drug response, etc. PATHiVar is available at: http://pathivar.babelomics.org.