Ralf Herwig

Analysis of Oct4‐Dependent Transcriptional Networks Regulating Self‐Renewal and Pluripotency in Human Embryonic Stem Cells

The POU domain transcription factor OCT4 is a key regulator of pluripotency in the early mammalian embryo and is highly expressed in the inner cell mass of the blastocyst. Consistent with its essential role in maintaining pluripotency, Oct4 expression is rapidly downregulated during formation of the trophoblast lineage. To enhance our understanding of the molecular basis of this differentiation event in humans, we used a functional genomics approach involving RNA interference‐mediated suppression of OCT4 function in a human ESC line and analysis of the resulting transcriptional profiles to identify OCT4‐dependent genes in human cells. We detected altered expression of >1,000 genes, including targets regulated directly by OCT4 either positively (NANOG, SOX2, REX1, LEFTB, LEFTA/EBAF DPPA4, THY1, and TDGF1) or negatively (CDX2, EOMES, BMP4, TBX18, Brachyury [T], DKK1, HLX1, GATA6, ID2, and DLX5), as well as targets for the OCT4‐associated stem cell regulators SOX2 and NANOG. Our data set includes regulators of ACTIVIN, BMP, fibroblast growth factor, and WNT signaling. These pathways are implicated in regulating human ESC differentiation and therefore further validate the results of our analysis. In addition, we identified a number of differentially expressed genes that are involved in epigenetics, chromatin remodeling, apoptosis, and metabolism that may point to underlying molecular mechanisms that regulate pluripotency and trophoblast differentiation in humans. Significant concordance between this data set and previous comparisons between inner cell mass and trophectoderm in human embryos indicates that the study of human ESC differentiation in vitro represents a useful model of early embryonic differentiation in humans.

The ConsensusPathDB interaction database: 2013 update

Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB (http://ConsensusPathDB.org) that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.

ConsensusPathDB: toward a more complete picture of cell biology

ConsensusPathDB is a meta-database that integrates different types of functional interactions from heterogeneous interaction data resources. Physical protein interactions, metabolic and signaling reactions and gene regulatory interactions are integrated in a seamless functional association network that simultaneously describes multiple functional aspects of genes, proteins, complexes, metabolites, etc. With 155 432 human, 194 480 yeast and 13 648 mouse complex functional interactions (originating from 18 databases on human and eight databases on yeast and mouse interactions each), ConsensusPathDB currently constitutes the most comprehensive publicly available interaction repository for these species. The Web interface at http://cpdb.molgen.mpg.de offers different ways of utilizing these integrated interaction data, in particular with tools for visualization, analysis and interpretation of high-throughput expression data in the light of functional interactions and biological pathways.

ConsensusPathDB—a database for integrating human functional interaction networks

ConsensusPathDB is a database system for the integration of human functional interactions. Current knowledge of these interactions is dispersed in more than 200 databases, each having a specific focus and data format. ConsensusPathDB currently integrates the content of 12 different interaction databases with heterogeneous foci comprising a total of 26 133 distinct physical entities and 74 289 distinct functional interactions (protein–protein interactions, biochemical reactions, gene regulatory interactions), and covering 1738 pathways. We describe the database schema and the methods used for data integration. Furthermore, we describe the functionality of the ConsensusPathDB web interface, where users can search and visualize interaction networks, upload, modify and expand networks in BioPAX, SBML or PSI-MI format, or carry out over-representation analysis with uploaded identifier lists with respect to substructures derived from the integrated interaction network. The ConsensusPathDB database is available at: http://cpdb.molgen.mpg.de

Primary Differentiation in the Human Blastocyst: Comparative Molecular Portraits of Inner Cell Mass and Trophectoderm Cells

The primary differentiation event during mammalian development occurs at the blastocyst stage and leads to the delineation of the inner cell mass (ICM) and the trophectoderm (TE). We provide the first global mRNA expression data from immunosurgically dissected ICM cells, TE cells, and intact human blastocysts. Using a cDNA microarray composed of 15,529 cDNAs from known and novel genes, we identify marker transcripts specific to the ICM (e.g., OCT4/POU5F1, NANOG, HMGB1, and DPPA5) and TE (e.g., CDX2, ATP1B3, SFN, and IPL), in addition to novel ICM‐ and TE‐specific expressed sequence tags. The expression patterns suggest that the emergence of pluripotent ICM and TE cell lineages from the morula is controlled by metabolic and signaling pathways, which include inter alia, WNT, mitogen‐activated protein kinase, transforming growth factor‐beta, NOTCH, integrin‐mediated cell adhesion, phosphatidylinositol 3‐kinase, and apoptosis. These data enhance our understanding of the first step in human cellular differentiation and, hence, the derivation of both embryonic stem cells and trophoblastic stem cells from these lineages.

Computational analysis of genome-wide DNA methylation during the differentiation of human embryonic stem cells along the endodermal lineage

The generation of genome-wide data derived from methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) has become a major tool for epigenetic studies in health and disease. The computational analysis of such data, however, still falls short on accuracy, sensitivity, and speed. We propose a time-efficient statistical method that is able to cope with the inherent complexity of MeDIP-seq data with similar performance compared with existing methods. In order to demonstrate the computational approach, we have analyzed alterations in DNA methylation during the differentiation of human embryonic stem cells (hESCs) to definitive endoderm. We show improved correlation of normalized MeDIP-seq data in comparison to available whole-genome bisulfite sequencing data, and investigated the effect of differential methylation on gene expression. Furthermore, we analyzed the interplay between DNA methylation, histone modifications, and transcription factor binding and show that in contrast to de novo methylation, demethylation is mainly associated with regions of low CpG densities.

A gene expression map of human chromosome 21 orthologues in the mouse

The DNA sequence of human chromosome 21 (HSA21)1 has opened the route for a systematic molecular characterization of all of its genes. Trisomy 21 is associated with Downs syndrome, the most common genetic cause of mental retardation in humans. The phenotype includes various organ dysmorphies, stereotypic craniofacial anomalies and brain malformations2. Molecular analysis of congenital aneuploidies poses a particular challenge because the aneuploid region contains many protein-coding genes whose function is unknown. One essential step towards understanding their function is to analyse mRNA expression patterns at key stages of organism development. Seminal works in flies, frogs and mice showed that genes whose expression is restricted spatially and/or temporally are often linked with specific ontogenic processes. Here we describe expression profiles of mouse orthologues to HSA21 genes by a combination of large-scale mRNA in situ hybridization at critical stages of embryonic and brain development and in silico (computed) mining of expressed sequence tags. This chromosome-scale expression annotation associates many of the genes tested with a potential biological role and suggests candidates for the pathogenesis of Downs syndrome.The DNA sequence of human chromosome 21 (HSA21) has opened the route for a systematic molecular characterization of all of its genes. Trisomy 21 is associated with Downs syndrome, the most common genetic cause of mental retardation in humans. The phenotype includes various organ dysmorphies, stereotypic craniofacial anomalies and brain malformations. Molecular analysis of congenital aneuploidies poses a particular challenge because the aneuploid region contains many protein-coding genes whose function is unknown. One essential step towards understanding their function is to analyse mRNA expression patterns at key stages of organism development. Seminal works in flies, frogs and mice showed that genes whose expression is restricted spatially and/or temporally are often linked with specific ontogenic processes. Here we describe expression profiles of mouse orthologues to HSA21 genes by a combination of large-scale mRNA in situ hybridization at critical stages of embryonic and brain development and in silico (computed) mining of expressed sequence tags. This chromosome-scale expression annotation associates many of the genes tested with a potential biological role and suggests candidates for the pathogenesis of Downs syndrome.

MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments

Motivation: DNA enrichment followed by sequencing is a versatile tool in molecular biology, with a wide variety of applications including genome-wide analysis of epigenetic marks and mechanisms. A common requirement of these diverse applications is a comparison of read coverage between experimental conditions. The amount of samples generated for such comparisons ranges from few replicates to hundreds of samples per condition for epigenome-wide association studies. Consequently, there is an urgent need for software that allows for fast and simple processing and comparison of sequencing data derived from enriched DNA. Results: Here, we present a major update of the R/Bioconductor package MEDIPS, which allows for an arbitrary number of replicates per group and integrates sophisticated statistical methods for the detection of differential coverage between experimental conditions. Our approach can be applied to a diversity of quantitative sequencing data. In addition, our update adds novel functionality to MEDIPS, including correlation analysis between samples, and takes advantage of Bioconductor’s annotation databases to facilitate annotation of specific genomic regions. Availability and implementation: The latest version of MEDIPS is available as version 1.12.0 and part of Bioconductor 2.13. The package comes with a manual containing detailed description of its functionality and is available at http://www.bioconductor.org. Contact: lienhard@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Targeted high throughput sequencing in clinical cancer Settings: formaldehyde fixed-paraffin embedded (FFPE) tumor tissues, input amount and tumor heterogeneity

BackgroundMassively parallel sequencing technologies have brought an enormous increase in sequencing throughput. However, these technologies need to be further improved with regard to reproducibility and applicability to clinical samples and settings.MethodsUsing identification of genetic variations in prostate cancer as an example we address three crucial challenges in the field of targeted re-sequencing: Small nucleotide variation (SNV) detection in samples of formalin-fixed paraffin embedded (FFPE) tissue material, minimal amount of input sample and sampling in view of tissue heterogeneity.ResultsWe show that FFPE tissue material can supplement for fresh frozen tissues for the detection of SNVs and that solution-based enrichment experiments can be accomplished with small amounts of DNA with only minimal effects on enrichment uniformity and data variance.Finally, we address the question whether the heterogeneity of a tumor is reflected by different genetic alterations, e.g. different foci of a tumor display different genomic patterns. We show that the tumor heterogeneity plays an important role for the detection of copy number variations.ConclusionsThe application of high throughput sequencing technologies in cancer genomics opens up a new dimension for the identification of disease mechanisms. In particular the ability to use small amounts of FFPE samples available from surgical tumor resections and histopathological examinations facilitates the collection of precious tissue materials. However, care needs to be taken in regard to the locations of the biopsies, which can have an influence on the prediction of copy number variations. Bearing these technological challenges in mind will significantly improve many large-scale sequencing studies and will - in the long term - result in a more reliable prediction of individual cancer therapies.

Renal tumour size measured radiologically before surgery is an unreliable variable for predicting histopathological features: benign tumours are not necessarily small

To compare histopathological findings as a function of radiological tumour size, as published data suggest that small renal tumours are often benign and large tumours are renal cell cancer (RCC).