Transcriptomic dynamics of breast cancer progression in the MMTV-PyMT mouse model.

BACKGROUND: Malignant breast cancer with complex molecular mechanisms of progression and metastasis remains a leading cause of death in women. To improve diagnosis and drug development, it is critical to identify panels of genes and molecular pathways involved in tumor progression and malignant transition. Using the PyMT mouse, a genetically engineered mouse model that has been widely used to study human breast cancer, we profiled and analyzed gene expression from four distinct stages of tumor progression (hyperplasia, adenoma/MIN, early carcinoma and late carcinoma) during which malignant transition occurs. RESULTS: We found remarkable expression similarity among the four stages, meaning genes altered in the later stages showed trace in the beginning of tumor progression. We identified a large number of differentially expressed genes in PyMT samples of all stages compared with normal mammary glands, enriched in cancer-related pathways. Using co-expression networks, we found panels of genes as signature modules with some hub genes that predict metastatic risk. Time-course analysis revealed genes with expression transition when shifting to malignant stages. These may provide additional insight into the molecular mechanisms beyond pathways. CONCLUSIONS: Thus, in this study, our various analyses with the PyMT mouse model shed new light on transcriptomic dynamics during breast cancer malignant progression.

Distinct Molecular Signature of Murine Fetal Liver and Adult Hematopoietic Stem Cells Identify Novel Regulators of Hematopoietic Stem Cell Function.

During ontogeny, fetal liver (FL) acts as a major site for hematopoietic stem cell (HSC) maturation and expansion, whereas HSCs in the adult bone marrow (ABM) are largely quiescent. HSCs in the FL possess faster repopulation capacity as compared with ABM HSCs. However, the molecular mechanism regulating the greater self-renewal potential of FL HSCs has not yet extensively been assessed. Recently, we published RNA sequencing-based gene expression analysis on FL HSCs from 14.5-day mouse embryo (E14.5) in comparison to the ABM HSCs. We reanalyzed these data to identify key transcriptional regulators that play important roles in the expansion of HSCs during development. The comparison of FL E14.5 with ABM HSCs identified more than 1,400 differentially expressed genes. More than 200 genes were shortlisted based on the gene ontology (GO) annotation term "transcription." By morpholino-based knockdown studies in zebrafish, we assessed the function of 18 of these regulators, previously not associated with HSC proliferation. Our studies identified a previously unknown role for tdg, uhrf1, uchl5, and ncoa1 in the emergence of definitive hematopoiesis in zebrafish. In conclusion, we demonstrate that identification of genes involved in transcriptional regulation differentially expressed between expanding FL HSCs and quiescent ABM HSCs, uncovers novel regulators of HSC function.

Integrated Post-GWAS Analysis Sheds New Light on the Disease Mechanisms of Schizophrenia.

Schizophrenia is a severe mental disorder with a large genetic component. Recent genome-wide association studies (GWAS) have identified many schizophrenia-associated common variants. For most of the reported associations, however, the underlying biological mechanisms are not clear. The critical first step for their elucidation is to identify the most likely disease genes as the source of the association signals. Here, we describe a general computational framework of post-GWAS analysis for complex disease gene prioritization. We identify 132 putative schizophrenia risk genes in 76 risk regions spanning 120 schizophrenia-associated common variants, 78 of which have not been recognized as schizophrenia disease genes by previous GWAS. Even more significantly, 29 of them are outside the risk regions, likely under regulation of transcriptional regulatory elements contained therein. These putative schizophrenia risk genes are transcriptionally active in both brain and the immune system, and highly enriched among cellular pathways, consistent with leading pathophysiological hypotheses about the pathogenesis of schizophrenia. With their involvement in distinct biological processes, these putative schizophrenia risk genes, with different association strengths, show distinctive temporal expression patterns, and play specific biological roles during brain development.

MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNA molecules of about 22 nucleotides which function to silence the expression of their target genes. Numerous studies have shown that miRNAs are not only key regulators in important cellular processes but are also drivers in the development of many diseases, especially cancer. Estrogen receptor positive luminal B is the second most common but the least studied subtype of breast cancer. Only a few studies have examined the expression profiles of miRNAs in luminal B breast cancer, and their regulatory roles in cancer progression have yet to be investigated. METHODS: In this study, using polyoma middle T antigen (PyMT) mice, a widely used luminal B breast cancer model, we profiled microRNA (miRNA) expression at four time points that represent different key developmental stages of cancer progression. We considered the expression of both miRNAs and messenger RNAs (mRNAs) at these time points to improve the identification of regulatory targets of miRNAs. By combining gene functional and pathway annotation with miRNA-mRNA interactions, we created a PyMT-specific tripartite miRNA-mRNA-pathway network and identified novel functional regulatory programs (FRPs). RESULTS: We identified 151 differentially expressed miRNAs with a strict dual nature of either upregulation or downregulation during the whole course of disease progression. Among 82 newly discovered breast-cancer-related miRNAs, 35 can potentially regulate 271 protein-coding genes based on their sequence complementarity and expression profiles. We also identified miRNA-mRNA regulatory modules driving specific cancer-related biological processes. CONCLUSIONS: In this study we profiled the expression of miRNAs during breast cancer progression in the PyMT mouse model. By integrating miRNA and mRNA expression profiles, we identified differentially expressed miRNAs and their target genes involved in several hallmarks of cancer. We applied a novel clustering method to an annotated miRNA-mRNA regulatory network and identified network modules involved in specific cancer-related biological processes.

Systems-level analysis of human aging genes shed new light on mechanisms of aging.

Although studies over the last decades have firmly connected a number of genes and molecular pathways to aging, the aging process as a whole still remains poorly understood. To gain novel insights into the mechanisms underlying aging, instead of considering aging genes individually, we studied their characteristics at the systems level in the context of biological networks. We calculated a comprehensive set of network characteristics for human aging-related genes from the GenAge database. By comparing them with other functional groups of genes, we identified a robust group of aging-specific network characteristics. To find the structural basis and the molecular mechanisms underlying this aging-related network specificity, we also analyzed protein domain interactions and gene expression patterns across different tissues. Our study revealed that aging genes not only tend to be network hubs, playing important roles in communication among different functional modules or pathways, but also are more likely to physically interact and be co-expressed with essential genes. The high expression of aging genes across a large number of tissue types also points to a high level of connectivity among aging genes. Unexpectedly, contrary to the depletion of interactions among hub genes in biological networks, we observed close interactions among aging hubs, which renders the aging subnetworks vulnerable to random attacks and thus may contribute to the aging process. Comparison across species reveals the evolution process of the aging subnetwork. As the organisms become more complex, the complexity of its aging mechanisms increases and their aging hub genes are more functionally connected.

A subpopulation of CD103(pos) ICOS(pos) Treg cells occurs at high frequency in lymphopenic mice and represents a lymph node specific differentiation stage.

Regulatory T (Treg) cells are pivotal for the maintenance of peripheral tolerance by controlling self-reactive, chronic, and homeostatic T-cell responses. Here, we report that the increase in Treg-cell suppressive function observed in lymphopenic mice correlates with the degree of lymphopenia and is caused by a higher frequency of a novel subpopulation of CD103(pos) ICOS(pos) Treg cells. Though present in the thymus, CD103(pos) ICOS(pos) Treg cells are not generated there but recirculate from the periphery to that site. The acquisition and maintenance of this distinctive phenotype requires the LN microenvironment and the in situ availability of antigen. Contrary to conventional effector and other Treg cells, the cellularity of CD103(pos) ICOS(pos) Treg cells is not affected by the absence of IL-7 and thymic stroma lymphopoetin. Given their increased frequency in lymphopenia, the absolute number of CD103(pos) ICOS(pos) Treg cells remains unchanged in the periphery irrespective of a paucity of total Treg cells. We furthermore demonstrate, with cell transfers in mice, that the CD103(pos) ICOS(pos) phenotype represents a LN-specific differentiation stage arrived at by several other Treg-cell subsets. Thus, tissue-specific cues determine the overall potency of the peripheral Treg-cell pool by shaping its subset composition.

miR-217 is an oncogene that enhances the germinal center reaction.

microRNAs are a class of regulators of gene expression that have been shown critical for a great number of biological processes; however, little is known of their role in germinal center (GC) B cells. Although the GC reaction is crucial to ensure a competent immune response, GC B cells are also the origin of most human lymphomas, presumably due to bystander effects of the immunoglobulin gene remodeling that takes place at these sites. Here we report that miR-217 is specifically upregulated in GC B cells. Gain- and loss-of-function mouse models reveal that miR-217 is a positive modulator of the GC response that increases the generation of class-switched antibodies and the frequency of somatic hypermutation. We find that miR-217 down-regulates the expression of a DNA damage response and repair gene network and in turn stabilizes Bcl-6 expression in GC B cells. Importantly, miR-217 overexpression also promotes mature B-cell lymphomagenesis; this is physiologically relevant as we find that miR-217 is overexpressed in aggressive human B-cell lymphomas. Therefore, miR-217 provides a novel molecular link between the normal GC response and B-cell transformation.

CentrosomeDB: a new generation of the centrosomal proteins database for Human and Drosophila melanogaster.

We present the second generation of centrosomeDB, available online at http://centrosome.cnb.csic.es, with a significant expansion of 1357 human and drosophila centrosomal genes and their corresponding information. The centrosome of animal cells takes part in important biological processes such as the organization of the interphase microtubule cytoskeleton and the assembly of the mitotic spindle. The active research done during the past decades has produced lots of data related to centrosomal proteins. Unfortunately, the accumulated data are dispersed among diverse and heterogeneous sources of information. We believe that the availability of a repository collecting curated evidences of centrosomal proteins would constitute a key resource for the scientific community. This was our first motivation to introduce CentrosomeDB in NAR database issue in 2009, collecting a set of human centrosomal proteins that were reported in the literature and other sources. The intensive use of this resource during these years has encouraged us to present this new expanded version. Using our database, the researcher is offered the possibility to study the evolution, function and structure of the centrosome. We have compiled information from many sources, including Gene Ontology, disease-association, single nucleotide polymorphisms and associated gene expression experiments. Special interest has been paid to protein-protein interaction.

MicroRNAs control the maintenance of thymic epithelia and their competence for T lineage commitment and thymocyte selection.

Thymic epithelial cells provide unique cues for the lifelong selection and differentiation of a repertoire of functionally diverse T cells. Rendered microRNA (miRNA) deficient, these stromal cells in the mouse lose their capacity to instruct the commitment of hematopoietic precursors to a T cell fate, to effect thymocyte positive selection, and to achieve promiscuous gene expression required for central tolerance induction. Over time, the microenvironment created by miRNA-deficient thymic epithelia assumes the cellular composition and structure of peripheral lymphoid tissue, where thympoiesis fails to be supported. These findings emphasize a global role for miRNA in the maintenance and function of the thymic epithelial cell scaffold and establish a novel mechanism how these cells control peripheral tissue Ag expression to prompt central immunological tolerance.

GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics.

Since its first release in 2007, GeneCodis has become a valuable tool to functionally interpret results from experimental techniques in genomics. This web-based application integrates different sources of information to finding groups of genes with similar biological meaning. This process, known as enrichment analysis, is essential in the interpretation of high-throughput experiments. The frequent feedbacks and the natural evolution of genomics and bioinformatics have allowed the growth of the tool and the development of this third release. In this version, a special effort has been made to remove noisy and redundant output from the enrichment results with the inclusion of a recently reported algorithm that summarizes significantly enriched terms and generates functionally coherent modules of genes and terms. A new comparative analysis has been added to allow the differential analysis of gene sets. To expand the scope of the application, new sources of biological information have been included, such as genetic diseases, drugs-genes interactions and Pubmed information among others. Finally, the graphic section has been renewed with the inclusion of new interactive graphics and filtering options. The application is freely available at http://genecodis.cnb.csic.es.

Quantification of miRNA-mRNA interactions.

miRNAs are small RNA molecules (' 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely. Recently, it has been proposed to combine expression measurement from both miRNA and mRNA and sequence based predictions to achieve more accurate relationships. In our work, we use LASSO regression with non-positive constraints to integrate both sources of information. LASSO enforces the sparseness of the solution and the non-positive constraints restrict the search of miRNA targets to those with down-regulation effects on the mRNA expression. We named this method TaLasso (miRNA-Target LASSO).We used TaLasso on two public datasets that have paired expression levels of human miRNAs and mRNAs. The top ranked interactions recovered by TaLasso are especially enriched (more than using any other algorithm) in experimentally validated targets. The functions of the genes with mRNA transcripts in the top-ranked interactions are meaningful. This is not the case using other algorithms.TaLasso is available as Matlab or R code. There is also a web-based tool for human miRNAs at http://talasso.cnb.csic.es/.

Functional analysis beyond enrichment: non-redundant reciprocal linkage of genes and biological terms.

Functional analysis of large sets of genes and proteins is becoming more and more necessary with the increase of experimental biomolecular data at omic-scale. Enrichment analysis is by far the most popular available methodology to derive functional implications of sets of cooperating genes. The problem with these techniques relies in the redundancy of resulting information, that in most cases generate lots of trivial results with high risk to mask the reality of key biological events. We present and describe a computational method, called GeneTerm Linker, that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. The algorithm is tested with a small set of well known interacting proteins from yeast and with a large collection of reference sets from three heterogeneous resources: multiprotein complexes (CORUM), cellular pathways (SGD) and human diseases (OMIM). Statistical Precision, Recall and balanced F-score are calculated showing robust results, even when different levels of random noise are included in the test sets. Although we could not find an equivalent method, we present a comparative analysis with a widely used method that combines enrichment and functional annotation clustering. A web application to use the method here proposed is provided at http://gtlinker.cnb.csic.es.

MARQ: an online tool to mine GEO for experiments with similar or opposite gene expression signatures.

The enormous amount of data available in public gene expression repositories such as Gene Expression Omnibus (GEO) offers an inestimable resource to explore gene expression programs across several organisms and conditions. This information can be used to discover experiments that induce similar or opposite gene expression patterns to a given query, which in turn may lead to the discovery of new relationships among diseases, drugs or pathways, as well as the generation of new hypotheses. In this work, we present MARQ, a web-based application that allows researchers to compare a query set of genes, e.g. a set of over- and under-expressed genes, against a signature database built from GEO datasets for different organisms and platforms. MARQ offers an easy-to-use and integrated environment to mine GEO, in order to identify conditions that induce similar or opposite gene expression patterns to a given experimental condition. MARQ also includes additional functionalities for the exploration of the results, including a meta-analysis pipeline to find genes that are differentially expressed across different experiments. The application is freely available at http://marq.dacya.ucm.es.

Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling.

Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein-protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.

Proteopathogen, a protein database for studying Candida albicans--host interaction.

There exist, at present, public web repositories for management and storage of proteomic data and also fungi-specific databases. None of them, however, is focused to the specific research area of fungal pathogens and their interactions with the host, and contains proteomics experimental data. In this context, we present Proteopathogen, a database intended to compile proteomics experimental data and to facilitate storage and access to a range of data which spans proteomics workflows from description of the experimental approaches leading to sample preparation to MS settings and peptides supporting protein identification. Proteopathogen is currently focused on Candida albicans and its interaction with macrophages; however, data from experiments concerning different pathogenic fungi species and other mammalian cells may also be found suitable for inclusion into the database. Proteopathogen is publicly available at http://proteopathogen.dacya.ucm.es.

GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biological information.

GeneCodis is a web server application for functional analysis of gene lists that integrates different sources of information and finds modular patterns of interrelated annotations. This integrative approach has proved to be useful for the interpretation of high-throughput experiments and therefore a new version of the system has been developed to expand its functionality and scope. GeneCodis now expands the functional information with regulatory patterns and user-defined annotations, offering the possibility of integrating all sources of information in the same analysis. Traditional singular enrichment is now permitted and more organisms and gene identifiers have been added to the database. The application has been re-engineered to improve performance, accessibility and scalability. In addition, GeneCodis can now be accessed through a public SOAP web services interface, enabling users to perform analysis from their own scripts and workflows. The application is freely available at http://genecodis.dacya.ucm.es.

SENT: semantic features in text.

We present SENT (semantic features in text), a functional interpretation tool based on literature analysis. SENT uses Non-negative Matrix Factorization to identify topics in the scientific articles related to a collection of genes or their products, and use them to group and summarize these genes. In addition, the application allows users to rank and explore the articles that best relate to the topics found, helping put the analysis results into context. This approach is useful as an exploratory step in the workflow of interpreting and understanding experimental data, shedding some light into the complex underlying biological mechanisms. This tool provides a user-friendly interface via a web site, and a programmatic access via a SOAP web server. SENT is freely accessible at http://sent.dacya.ucm.es.

CentrosomeDB: a human centrosomal proteins database.

Active research on the biology of the centrosome during the past decades has allowed the identification and characterization of many centrosomal proteins. Unfortunately, the accumulated data is still dispersed among heterogeneous sources of information. Here we present centrosome:db, which intends to compile and integrate relevant information related to the human centrosome. We have compiled a set of 383 likely human centrosomal genes and recorded the associated supporting evidences. Centrosome:db offers several perspectives to study the human centrosome including evolution, function and structure. The database contains information on the orthology relationships with other species, including fungi, nematodes, arthropods, urochordates and vertebrates. Predictions of the domain organization of centrosome:db proteins are graphically represented at different sections of the database, including sets of alternative protein isoforms, interacting proteins, groups of orthologs and the homologs identified with blast. Centrosome:db also contains information related to function, gene-disease associations, SNPs and the 3D structure of proteins. Apart from important differences in the coverage of the set of centrosomal genes, our database differentiates from other similar initiatives in the way information is treated and analyzed. Centrosome:db is publicly available at http://centrosome.dacya.ucm.es.