Disrupted PGR-B and ESR1 signaling underlies defective decidualization linked to severe preeclampsia.

Background: Decidualization of the uterine mucosa drives the maternal adaptation to invasion by the placenta. Appropriate depth of placental invasion is needed to support a healthy pregnancy; shallow invasion is associated with the development of severe preeclampsia (sPE). Maternal contribution to sPE through failed decidualization is an important determinant of placental phenotype. However, the molecular mechanism underlying the in vivo defect linking decidualization to sPE is unknown. Methods: Global RNA sequencing was applied to obtain the transcriptomic profile of endometrial biopsies collected from nonpregnant women who suffer sPE in a previous pregnancy and women who did not develop this condition. Samples were randomized in two cohorts, the training and the test set, to identify the fingerprinting encoding defective decidualization in sPE and its subsequent validation. Gene Ontology enrichment and an interaction network were performed to deepen in pathways impaired by genetic dysregulation in sPE. Finally, the main modulators of decidualization, estrogen receptor 1 (ESR1) and progesterone receptor B (PGR-B), were assessed at the level of gene expression and protein abundance. Results: Here, we discover the footprint encoding this decidualization defect comprising 120 genes-using global gene expression profiling in decidua from women who developed sPE in a previous pregnancy. This signature allowed us to effectively segregate samples into sPE and control groups. ESR1 and PGR were highly interconnected with the dynamic network of the defective decidualization fingerprint. ESR1 and PGR-B gene expression and protein abundance were remarkably disrupted in sPE. Conclusions: Thus, the transcriptomic signature of impaired decidualization implicates dysregulated hormonal signaling in the decidual endometria in women who developed sPE. These findings reveal a potential footprint that could be leveraged for a preconception or early prenatal screening of sPE risk, thus improving prevention and early treatments. Funding: This work has been supported by the grant PI19/01659 (MCIU/AEI/FEDER, UE) from the Spanish Carlos III Institute awarded to TGG. NCM was supported by the PhD program FDGENT/2019/008 from the Spanish Generalitat Valenciana. IMB was supported by the PhD program PRE2019-090770 and funding was provided by the grant RTI2018-094946-B-100 (MCIU/AEI/FEDER, UE) from the Spanish Ministry of Science and Innovation with CS as principal investigator. This research was funded partially by Igenomix S.L.

Identification and Characterization of Extracellular Vesicles and Its DNA Cargo Secreted During Murine Embryo Development.

Extracellular vesicles (EVs) are known to transport DNA, but their implications in embryonic implantation are unknown. The aim of this study was to investigate EVs production and secretion by preimplantation embryos and assess their DNA cargo. Murine oocytes and embryos were obtained from six- to eight-week-old females, cultured until E4.5 and analyzed using transmission electron microscopy to examine EVs production. EVs were isolated from E4.5-day conditioned media and quantified by nanoparticle tracking analysis, characterized by immunogold, and their DNA cargo sequenced. Multivesicular bodies were observed in murine oocytes and preimplantation embryos together with the secretion of EVs to the blastocoel cavity and blastocyst spent medium. Embryo-derived EVs showed variable electron-densities and sizes (20-500 nm) and total concentrations of 1.74 × 107 ± 2.60 × 106 particles/mL. Embryo secreted EVs were positive for CD63 and ARF6. DNA cargo sequencing demonstrated no differences in DNA between apoptotic bodies or smaller EVs, although they showed significant gene enrichment compared to control medium. The analysis of sequences uniquely mapping the murine genome revealed that DNA contained in EVs showed higher representation of embryo genome than vesicle-free DNA. Murine blastocysts secrete EVs containing genome-wide sequences of DNA to the medium, reinforcing the relevance of studying these vesicles and their cargo in the preimplantation moment, where secreted DNA may help the assessment of the embryo previous to implantation.

Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.

In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.

A comparison of mechanistic signaling pathway activity analysis methods.

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.

BACKGROUND: Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases. RESULTS: Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression. CONCLUSION: We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker. REVIEWERS: This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.

Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.

Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies.Significance: Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. Cancer Res; 78(21); 6059-72. ©2018 AACR.

The effects of death and post-mortem cold ischemia on human tissue transcriptomes.

Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.

Reference genome assessment from a population scale perspective: an accurate profile of variability and noise.

MOTIVATION: Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome. RESULTS: The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples. AVAILABILITY AND IMPLEMENTATION: This tool is freely available at http://gitlab.com/carbonell/ces. CONTACT: jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.

A Long Noncoding RNA Regulates Sister Chromatid Cohesion.

Long noncoding RNAs (lncRNAs) are involved in diverse cellular processes through multiple mechanisms. Here, we describe a previously uncharacterized human lncRNA, CONCR (cohesion regulator noncoding RNA), that is transcriptionally activated by MYC and is upregulated in multiple cancer types. The expression of CONCR is cell cycle regulated, and it is required for cell-cycle progression and DNA replication. Moreover, cells depleted of CONCR show severe defects in sister chromatid cohesion, suggesting an essential role for CONCR in cohesion establishment during cell division. CONCR interacts with and regulates the activity of DDX11, a DNA-dependent ATPase and helicase involved in DNA replication and sister chromatid cohesion. These findings unveil a direct role for an lncRNA in the establishment of sister chromatid cohesion by modulating DDX11 enzymatic activity.

Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models.

The discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.

267 Spanish Exomes Reveal Population-Specific Differences in Disease-Related Genetic Variation.

Recent results from large-scale genomic projects suggest that allele frequencies, which are highly relevant for medical purposes, differ considerably across different populations. The need for a detailed catalog of local variability motivated the whole-exome sequencing of 267 unrelated individuals, representative of the healthy Spanish population. Like in other studies, a considerable number of rare variants were found (almost one-third of the described variants). There were also relevant differences in allelic frequencies in polymorphic variants, including ∼10,000 polymorphisms private to the Spanish population. The allelic frequencies of variants conferring susceptibility to complex diseases (including cancer, schizophrenia, Alzheimer disease, type 2 diabetes, and other pathologies) were overall similar to those of other populations. However, the trend is the opposite for variants linked to Mendelian and rare diseases (including several retinal degenerative dystrophies and cardiomyopathies) that show marked frequency differences between populations. Interestingly, a correspondence between differences in allelic frequencies and disease prevalence was found, highlighting the relevance of frequency differences in disease risk. These differences are also observed in variants that disrupt known drug binding sites, suggesting an important role for local variability in population-specific drug resistances or adverse effects. We have made the Spanish population variant server web page that contains population frequency information for the complete list of 170,888 variant positions we found publicly available (http://spv.babelomics.org/), We show that it if fundamental to determine population-specific variant frequencies to distinguish real disease associations from population-specific polymorphisms.

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).

Babelomics 5.0: functional interpretation for new generations of genomic data.

Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.

A novel approach to identify candidate prognostic factors for hepatitis C treatment response integrating clinical and viral genetic data.

The combined therapy of pegylated interferon (IFN) plus ribavirin (RBV) has been for a long time the standard treatment for patients infected with hepatitis C virus (HCV). In the case of genotype 1, only 38%-48% of patients have a positive response to the combined treatment. In previous studies, viral genetic information has been occasionally included as a predictor. Here, we consider viral genetic variation in addition to 11 clinical and 19 viral populations and evolutionary parameters to identify candidate baseline prognostic factors that could be involved in the treatment outcome. We obtained potential prognostic models for HCV subtypes la and lb in combination as well as separately. We also found that viral genetic information is relevant for the combined treatment assessment of patients, as the potential prognostic model of joint subtypes includes 9 viral-related variables out of 11. Our proposed methodology fully characterizes viral genetic information and finds a combination of positions that modulate inter-patient variability.

Understanding disease mechanisms with models of signaling pathway activities.

BACKGROUND: Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine. RESULTS: Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets. CONCLUSIONS: The proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.

Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.

Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.

epiPATH: an information system for the storage and management of molecular epidemiology data from infectious pathogens.

BACKGROUND: Most research scientists working in the fields of molecular epidemiology, population and evolutionary genetics are confronted with the management of large volumes of data. Moreover, the data used in studies of infectious diseases are complex and usually derive from different institutions such as hospitals or laboratories. Since no public database scheme incorporating clinical and epidemiological information about patients and molecular information about pathogens is currently available, we have developed an information system, composed by a main database and a web-based interface, which integrates both types of data and satisfies requirements of good organization, simple accessibility, data security and multi-user support. RESULTS: From the moment a patient arrives to a hospital or health centre until the processing and analysis of molecular sequences obtained from infectious pathogens in the laboratory, lots of information is collected from different sources. We have divided the most relevant data into 12 conceptual modules around which we have organized the database schema. Our schema is very complete and it covers many aspects of sample sources, samples, laboratory processes, molecular sequences, phylogenetics results, clinical tests and results, clinical information, treatments, pathogens, transmissions, outbreaks and bibliographic information. Communication between end-users and the selected Relational Database Management System (RDMS) is carried out by default through a command-line window or through a user-friendly, web-based interface which provides access and management tools for the data. CONCLUSION: epiPATH is an information system for managing clinical and molecular information from infectious diseases. It facilitates daily work related to infectious pathogens and sequences obtained from them. This software is intended for local installation in order to safeguard private data and provides advanced SQL-users the flexibility to adapt it to their needs. The database schema, tool scripts and web-based interface are free software but data stored in our database server are not publicly available. epiPATH is distributed under the terms of GNU General Public License. More details about epiPATH can be found at http://genevo.uv.es/epipath.