Enrique M. Muro

Gene function in early mouse embryonic stem cell differentiation

BackgroundLittle is known about the genes that drive embryonic stem cell differentiation. However, such knowledge is necessary if we are to exploit the therapeutic potential of stem cells. To uncover the genetic determinants of mouse embryonic stem cell (mESC) differentiation, we have generated and analyzed 11-point time-series of DNA microarray data for three biologically equivalent but genetically distinct mESC lines (R1, J1, and V6.5) undergoing undirected differentiation into embryoid bodies (EBs) over a period of two weeks.ResultsWe identified the initial 12 hour period as reflecting the early stages of mESC differentiation and studied probe sets showing consistent changes of gene expression in that period. Gene function analysis indicated significant up-regulation of genes related to regulation of transcription and mRNA splicing, and down-regulation of genes related to intracellular signaling. Phylogenetic analysis indicated that the genes showing the largest expression changes were more likely to have originated in metazoans. The probe sets with the most consistent gene changes in the three cell lines represented 24 down-regulated and 12 up-regulated genes, all with closely related human homologues. Whereas some of these genes are known to be involved in embryonic developmental processes (e.g. Klf4, Otx2, Smn1, Socs3, Tagln, Tdgf1), our analysis points to others (such as transcription factor Phf21a, extracellular matrix related Lama1 and Cyr61, or endoplasmic reticulum related Sc4mol and Scd2) that have not been previously related to mESC function. The majority of identified functions were related to transcriptional regulation, intracellular signaling, and cytoskeleton. Genes involved in other cellular functions important in ESC differentiation such as chromatin remodeling and transmembrane receptors were not observed in this set.ConclusionOur analysis profiles for the first time gene expression at a very early stage of mESC differentiation, and identifies a functional and phylogenetic signature for the genes involved. The data generated constitute a valuable resource for further studies. All DNA microarray data used in this study are available in the StemBase database of stem cell gene expression data [1] and in the NCBIs GEO database.

MedlineRanker: flexible ranking of biomedical literature

The biomedical literature is represented by millions of abstracts available in the Medline database. These abstracts can be queried with the PubMed interface, which provides a keyword-based Boolean search engine. This approach shows limitations in the retrieval of abstracts related to very specific topics, as it is difficult for a non-expert user to find all of the most relevant keywords related to a biomedical topic. Additionally, when searching for more general topics, the same approach may return hundreds of unranked references. To address these issues, text mining tools have been developed to help scientists focus on relevant abstracts. We have implemented the MedlineRanker webserver, which allows a flexible ranking of Medline for a topic of interest without expert knowledge. Given some abstracts related to a topic, the program deduces automatically the most discriminative words in comparison to a random selection. These words are used to score other abstracts, including those from not yet annotated recent publications, which can be then ranked by relevance. We show that our tool can be highly accurate and that it is able to process millions of abstracts in a practical amount of time. MedlineRanker is free for use and is available at http://cbdm.mdc-berlin.de/tools/medlineranker.

Study of stem cell function using microarray experiments

DNA Microarrays are used to simultaneously measure the levels of thousands of mRNAs in a sample. We illustrate here that a collection of such measurements in different cell types and states is a sound source of functional predictions, provided the microarray experiments are analogous and the cell samples are appropriately diverse. We have used this approach to study stem cells, whose identity and mechanisms of control are not well understood, generating Affymetrix microarray data from more than 200 samples, including stem cells and their derivatives, from human and mouse. The data can be accessed online (StemBase; http://www.scgp.ca:8080/StemBase/).

Identification of gene 3′ ends by automated EST cluster analysis

The properties and biology of mRNA transcripts can be affected profoundly by the choice of alternative polyadenylation sites, making definition of the 3′ ends of transcripts essential for understanding their regulation. Here we show that 22–52% of sequences in commonly used human and murine “full-length” transcript databases may not currently end at bona fide polyadenylation sites. To identify probable transcript termini over the entire murine and human genomes, we analyzed the EST databases for positional clustering of EST ends. The analysis yielded 58,282 murine- and 86,410 human-candidate polyadenylation sites, of which 75% mapped to 23,091 known murine transcripts and 22,891 known human transcripts. The murine dataset correctly predicted 97% of the 3′ ends in a manually curated and experimentally supported benchmark transcript set. Of currently known genes, 15% had no associated prediction and 25% had only a single predicted termination site. The remaining genes had an average of 3–4 alternative polyadenylation sites predicted for each murine or human transcript, respectively. The results are made available in the form of tables and an interactive web site that can be mined for rapid assessment of the validity of 3′ ends in existing collections, enumeration of potential alternative 3′ polyadenylation sites of known transcripts, direct retrieval of terminal sequences for design of probes, and detection of polyadenylation sites not currently mapped to known genes.

StemBase: a resource for the analysis of stem cell gene expression data.

StemBase is a database of gene expression data obtained from stem cells and derivatives mainly from mouse and human using DNA microarrays and Serial Analysis of Gene Expression. Here, we describe this database and indicate ways to use it for the study the expression of particular genes in stem cells or to search for genes with particular expression profiles in stem cells, which could be associated to stem cell function or used as stem cell markers.

Computational approaches to discovering noncoding RNA.

New developments are being brought to the field of molecular biology with the mounting evidence that RNA transcripts not translated into protein (noncoding RNAs, ncRNAs) hold a variety of biological functions. Computational discovery of ncRNAs is one of these developments, fueled not only by the urge to characterize these sequences but also by necessity to prioritize ones with the most relevant functions for experimental verification. The heterogeneity in size and mode of activity of ncRNAs is reflected in the corresponding diversity of computational methods for their study. Sequence and structural analysis, conservation across species, and relative position to other genomic elements are being used for ncRNA detection. In addition, the recent development of techniques that allow deep sequencing of cell transcripts either globally or from isolated ncRNA‐related material is leading the field toward increased use of such high‐throughput data. We expect that imminent breakthroughs will include the classification of newer types of ncRNA and new insights into miRNA and piRNA biology, eventually leading toward the completion of a catalog of all human ncRNAs. WIREs RNA 2012, 3:567–579. doi: 10.1002/wrna.1121

Recent developments in StemBase: a tool to study gene expression in human and murine stem cells

BackgroundCurrently one of the largest online repositories for human and mouse stem cell gene expression data, StemBase was first designed as a simple web-interface to DNA microarray data generated by the Canadian Stem Cell Network to facilitate the discovery of gene functions relevant to stem cell control and differentiation.FindingsSince its creation, StemBase has grown in both size and scope into a system with analysis tools that examine either the whole database at once, or slices of data, based on tissue type, cell type or gene of interest. As of September 1, 2008, StemBase contains gene expression data (microarray and Serial Analysis of Gene Expression) from 210 stem cell samples in 60 different experiments.ConclusionStemBase can be used to study gene expression in human and murine stem cells and is available at http://www.stembase.ca.

Comprehensive translational control of tyrosine kinase expression by upstream open reading frames

Post-transcriptional control has emerged as a major regulatory event in gene expression and often occurs at the level of translation initiation. Although overexpression or constitutive activation of tyrosine kinases (TKs) through gene amplification, translocation or mutation are well-characterized oncogenic events, current knowledge about translational mechanisms of TK activation is scarce. Here, we report the presence of translational cis-regulatory upstream open reading frames (uORFs) in the majority of transcript leader sequences of human TK mRNAs. Genetic ablation of uORF initiation codons in TK transcripts resulted in enhanced translation of the associated downstream main protein-coding sequences (CDSs) in all cases studied. Similarly, experimental removal of uORF start codons in additional non-TK proto-oncogenes, and naturally occurring loss-of-uORF alleles of the c-met proto-oncogene (MET) and the kinase insert domain receptor (KDR), was associated with increased CDS translation. Based on genome-wide sequence analyses we identified polymorphisms in 15.9% of all human genes affecting uORF initiation codons, associated Kozak consensus sequences or uORF-related termination codons. Together, these data suggest a comprehensive role of uORF-mediated translational control and delineate how aberrant induction of proto-oncogenes through loss-of-function mutations at uORF initiation codons may be involved in the etiology of cancer. We provide a detailed map of uORFs across the human genome to stimulate future research on the pathogenic role of uORFs.

ChIP on SNP-chip for genome-wide analysis of human histone H4 hyperacetylation

BackgroundSNP microarrays are designed to genotype Single Nucleotide Polymorphisms (SNPs). These microarrays report hybridization of DNA fragments and therefore can be used for the purpose of detecting genomic fragments.ResultsHere, we demonstrate that a SNP microarray can be effectively used in this way to perform chromatin immunoprecipitation (ChIP) on chip as an alternative to tiling microarrays. We illustrate this novel application by mapping whole genome histone H4 hyperacetylation in human myoblasts and myotubes. We detect clusters of hyperacetylated histone H4, often spanning across up to 300 kilobases of genomic sequence. Using complementary genome-wide analyses of gene expression by DNA microarray we demonstrate that these clusters of hyperacetylated histone H4 tend to be associated with expressed genes.ConclusionThe use of a SNP array for a ChIP-on-chip application (ChIP on SNP-chip) will be of great value to laboratories whose interest is the determination of general rules regarding the relationship of specific chromatin modifications to transcriptional status throughout the genome and to examine the asymmetric modification of chromatin at heterozygous loci.

Co-regulation of paralog genes in the three-dimensional chromatin architecture

Paralog genes arise from gene duplication events during evolution, which often lead to similar proteins that cooperate in common pathways and in protein complexes. Consequently, paralogs show correlation in gene expression whereby the mechanisms of co-regulation remain unclear. In eukaryotes, genes are regulated in part by distal enhancer elements through looping interactions with gene promoters. These looping interactions can be measured by genome-wide chromatin conformation capture (Hi-C) experiments, which revealed self-interacting regions called topologically associating domains (TADs). We hypothesize that paralogs share common regulatory mechanisms to enable coordinated expression according to TADs. To test this hypothesis, we integrated paralogy annotations with human gene expression data in diverse tissues, genome-wide enhancer–promoter associations and Hi-C experiments in human, mouse and dog genomes. We show that paralog gene pairs are enriched for co-localization in the same TAD, share more often common enhancer elements than expected and have increased contact frequencies over large genomic distances. Combined, our results indicate that paralogs share common regulatory mechanisms and cluster not only in the linear genome but also in the three-dimensional chromatin architecture. This enables concerted expression of paralogs over diverse cell-types and indicate evolutionary constraints in functional genome organization.