Spatial multiomics map of trophoblast development in early pregnancy.

The relationship between the human placenta-the extraembryonic organ made by the fetus, and the decidua-the mucosal layer of the uterus, is essential to nurture and protect the fetus during pregnancy. Extravillous trophoblast cells (EVTs) derived from placental villi infiltrate the decidua, transforming the maternal arteries into high-conductance vessels1. Defects in trophoblast invasion and arterial transformation established during early pregnancy underlie common pregnancy disorders such as pre-eclampsia2. Here we have generated a spatially resolved multiomics single-cell atlas of the entire human maternal-fetal interface including the myometrium, which enables us to resolve the full trajectory of trophoblast differentiation. We have used this cellular map to infer the possible transcription factors mediating EVT invasion and show that they are preserved in in vitro models of EVT differentiation from primary trophoblast organoids3,4 and trophoblast stem cells5. We define the transcriptomes of the final cell states of trophoblast invasion: placental bed giant cells (fused multinucleated EVTs) and endovascular EVTs (which form plugs inside the maternal arteries). We predict the cell-cell communication events contributing to trophoblast invasion and placental bed giant cell formation, and model the dual role of interstitial EVTs and endovascular EVTs in mediating arterial transformation during early pregnancy. Together, our data provide a comprehensive analysis of postimplantation trophoblast differentiation that can be used to inform the design of experimental models of the human placenta in early pregnancy.

Single-cell roadmap of human gonadal development.

Gonadal development is a complex process that involves sex determination followed by divergent maturation into either testes or ovaries1. Historically, limited tissue accessibility, a lack of reliable in vitro models and critical differences between humans and mice have hampered our knowledge of human gonadogenesis, despite its importance in gonadal conditions and infertility. Here, we generated a comprehensive map of first- and second-trimester human gonads using a combination of single-cell and spatial transcriptomics, chromatin accessibility assays and fluorescent microscopy. We extracted human-specific regulatory programmes that control the development of germline and somatic cell lineages by profiling equivalent developmental stages in mice. In both species, we define the somatic cell states present at the time of sex specification, including the bipotent early supporting population that, in males, upregulates the testis-determining factor SRY and sPAX8s, a gonadal lineage located at the gonadal-mesonephric interface. In females, we resolve the cellular and molecular events that give rise to the first and second waves of granulosa cells that compartmentalize the developing ovary to modulate germ cell differentiation. In males, we identify human SIGLEC15+ and TREM2+ fetal testicular macrophages, which signal to somatic cells outside and inside the developing testis cords, respectively. This study provides a comprehensive spatiotemporal map of human and mouse gonadal differentiation, which can guide in vitro gonadogenesis.

Benchmark and integration of resources for the estimation of human transcription factor activities.

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF-target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.

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.

Web-based network analysis and visualization using CellMaps.

UNLABELLED: : CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them. Computations and analyses are remotely executed in high-end servers, and all the functionalities are available through RESTful web services. CellMaps can easily be integrated in any web page by using an available JavaScript API. AVAILABILITY AND IMPLEMENTATION: The application is available at: http://cellmaps.babelomics.org/ and the code can be found in: https://github.com/opencb/cell-maps The client is implemented in JavaScript and the server in C and Java. CONTACT: jdopazo@cipf.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins.

The post-translational regulation of proteins is mainly driven by two molecular events, their modification by several types of moieties and their interaction with other proteins. These two processes are interdependent and together are responsible for the function of the protein in a particular cell state. Several databases focus on the prediction and compilation of protein-protein interactions (PPIs) and no less on the collection and analysis of protein post-translational modifications (PTMs), however, there are no resources that concentrate on describing the regulatory role of PTMs in PPIs. We developed several methods based on residue co-evolution and proximity to predict the functional associations of pairs of PTMs that we apply to modifications in the same protein and between two interacting proteins. In order to make data available for understudied organisms, PTMcode v2 (http://ptmcode.embl.de) includes a new strategy to propagate PTMs from validated modified sites through orthologous proteins. The second release of PTMcode covers 19 eukaryotic species from which we collected more than 300,000 experimentally verified PTMs (>1,300,000 propagated) of 69 types extracting the post-translational regulation of >100,000 proteins and >100,000 interactions. In total, we report 8 million associations of PTMs regulating single proteins and over 9.4 million interplays tuning PPIs.

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 Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces.

Despite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated.

The role of the interactome in the maintenance of deleterious variability in human populations.

Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins.

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.

CellBase, a comprehensive collection of RESTful web services for retrieving relevant biological information from heterogeneous sources.

During the past years, the advances in high-throughput technologies have produced an unprecedented growth in the number and size of repositories and databases storing relevant biological data. Today, there is more biological information than ever but, unfortunately, the current status of many of these repositories is far from being optimal. Some of the most common problems are that the information is spread out in many small databases; frequently there are different standards among repositories and some databases are no longer supported or they contain too specific and unconnected information. In addition, data size is increasingly becoming an obstacle when accessing or storing biological data. All these issues make very difficult to extract and integrate information from different sources, to analyze experiments or to access and query this information in a programmatic way. CellBase provides a solution to the growing necessity of integration by easing the access to biological data. CellBase implements a set of RESTful web services that query a centralized database containing the most relevant biological data sources. The database is hosted in our servers and is regularly updated. CellBase documentation can be found at http://docs.bioinfo.cipf.es/projects/cellbase.

Pathogenic variants in the human m6A reader YTHDC2 are associated with primary ovarian insufficiency.

Primary ovarian insufficiency (POI) affects 1% of women and carries significant medical and psychosocial sequelae. Approximately 10% of POI has a defined genetic cause, with most implicated genes relating to biological processes involved in early fetal ovary development and function. Recently, Ythdc2, an RNA helicase and N6-methyladenosine reader, has emerged as a regulator of meiosis in mice. Here, we describe homozygous pathogenic variants in YTHDC2 in 3 women with early-onset POI from 2 families: c. 2567C>G, p.P856R in the helicase-associated (HA2) domain and c.1129G>T, p.E377*. We demonstrated that YTHDC2 is expressed in the developing human fetal ovary and is upregulated in meiotic germ cells, together with related meiosis-associated factors. The p.P856R variant resulted in a less flexible protein that likely disrupted downstream conformational kinetics of the HA2 domain, whereas the p.E377* variant truncated the helicase core. Taken together, our results reveal that YTHDC2 is a key regulator of meiosis in humans and pathogenic variants within this gene are associated with POI.

Single-cell Atlas of common variable immunodeficiency shows germinal center-associated epigenetic dysregulation in B-cell responses.

Common variable immunodeficiency (CVID), the most prevalent symptomatic primary immunodeficiency, displays impaired terminal B-cell differentiation and defective antibody responses. Incomplete genetic penetrance and ample phenotypic expressivity in CVID suggest the participation of additional pathogenic mechanisms. Monozygotic (MZ) twins discordant for CVID are uniquely valuable for studying the contribution of epigenetics to the disease. Here, we generate a single-cell epigenomics and transcriptomics census of naïve-to-memory B cell differentiation in a CVID-discordant MZ twin pair. Our analysis identifies DNA methylation, chromatin accessibility and transcriptional defects in memory B-cells mirroring defective cell-cell communication upon activation. These findings are validated in a cohort of CVID patients and healthy donors. Our findings provide a comprehensive multi-omics map of alterations in naïve-to-memory B-cell transition in CVID and indicate links between the epigenome and immune cell cross-talk. Our resource, publicly available at the Human Cell Atlas, gives insight into future diagnosis and treatments of CVID patients.

Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro.

The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones. We have generated dense single-cell and spatial reference maps of the human uterus and three-dimensional endometrial organoid cultures. We dissect the signaling pathways that determine cell fate of the epithelial lineages in the lumenal and glandular microenvironments. Our benchmark of the endometrial organoids reveals the pathways and cell states regulating differentiation of the secretory and ciliated lineages both in vivo and in vitro. In vitro downregulation of WNT or NOTCH pathways increases the differentiation efficiency along the secretory and ciliated lineages, respectively. We utilize our cellular maps to deconvolute bulk data from endometrial cancers and endometriotic lesions, illuminating the cell types dominating in each of these disorders. These mechanistic insights provide a platform for future development of treatments for common conditions including endometriosis and endometrial carcinoma.

The functional landscape of the human phosphoproteome.

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.

Pathway-based dissection of the genomic heterogeneity of cancer hallmarks' acquisition with SLAPenrich.

Cancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechanisms. Further, we explored the heterogeneity of these signatures, in terms of ratio of altered pathways associated with each individual hallmark, assuming that this is reflective of the extent of selective advantage provided to the cancer type under consideration. Our analysis revealed the predominance of certain hallmarks in specific cancer types, thus suggesting different evolutionary trajectories across cancer lineages. Finally, although many pathway alteration enrichments are guided by somatic mutations in frequently altered high-confidence cancer genes, excluding these driver mutations preserves the hallmark heterogeneity signatures, thus the detected hallmarks' predominance across cancer types. As a consequence, we propose the hallmark signatures as a ground truth to characterise tails of infrequent genomic alterations and identify potential novel cancer driver genes and networks.

The modular network structure of the mutational landscape of Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is associated with the sequential accumulation of acquired genetic alterations. Although at diagnosis cytogenetic alterations are frequent in AML, roughly 50% of patients present an apparently normal karyotype (NK), leading to a highly heterogeneous prognosis. Due to this significant heterogeneity, it has been suggested that different molecular mechanisms may trigger the disease with diverse prognostic implications. We performed whole-exome sequencing (WES) of tumor-normal matched samples of de novo AML-NK patients lacking mutations in NPM1, CEBPA or FLT3-ITD to identify new gene mutations with potential prognostic and therapeutic relevance to patients with AML. Novel candidate-genes, together with others previously described, were targeted resequenced in an independent cohort of 100 de novo AML patients classified in the cytogenetic intermediate-risk (IR) category. A mean of 4.89 mutations per sample were detected in 73 genes, 35 of which were mutated in more than one patient. After a network enrichment analysis, we defined a single in silico model and established a set of seed-genes that may trigger leukemogenesis in patients with normal karyotype. The high heterogeneity of gene mutations observed in AML patients suggested that a specific alteration could not be as essential as the interaction of deregulated pathways.

Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer.

Transcriptional dysregulation induced by aberrant transcription factors (TF) is a key feature of cancer, but its global influence on drug sensitivity has not been examined. Here, we infer the transcriptional activity of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 cancer cell lines, combined with publicly available datasets to survey a total of 1,056 cancer cell lines and 9,250 primary tumors. Predicted TF activities are supported by their agreement with independent shRNA essentiality profiles and homozygous gene deletions, and recapitulate mutant-specific mechanisms of transcriptional dysregulation in cancer. By analyzing cell line responses to 265 compounds, we uncovered numerous TFs whose activity interacts with anticancer drugs. Importantly, combining existing pharmacogenomic markers with TF activities often improves the stratification of cell lines in response to drug treatment. Our results, which can be queried freely at dorothea.opentargets.io, offer a broad foundation for discovering opportunities to refine personalized cancer therapies.Significance: Systematic analysis of transcriptional dysregulation in cancer cell lines and patient tumor specimens offers a publicly searchable foundation to discover new opportunities to refine personalized cancer therapies. Cancer Res; 78(3); 769-80. ©2017 AACR.