Transcriptional Regulatory Network Analysis Research Articles

Unraveling transcription regulatory networks by protein–DNA and protein–protein interaction mapping

Metazoan genomes contain thousands of protein-coding and noncoding RNA genes, most of which are differentially expressed, i.e., at different locations or at different times during development, function, or pathology of the organism. Differential gene expression is achieved in part by the action of regulatory transcription factors (TFs) that bind to cis-regulatory elements that are often located in or near their target genes. Each TF likely regulates many targets in the context of intricate transcription regulatory networks. Up to 10% of a genome may encode TFs, but only a handful of these have been studied in detail. Here, I will discuss the different steps involved in the mapping and analysis of transcription regulatory networks, including the identification of network nodes (TFs and their target sequences) and edges (TF-TF dimers and TF-DNA target interactions), integration with other data types, and network properties and emerging principles that provide insights into differential gene expression.

Transcriptional regulatory network analysis of developing human erythroid progenitors reveals patterns of coregulation and potential transcriptional regulators

Deciphering the molecular basis for human erythropoiesis should yield information benefiting studies of the hemoglobinopathies and other erythroid disorders. We used an in vitro erythroid differentiation system to study the developing red blood cell transcriptome derived from adult CD34+ hematopoietic progenitor cells. mRNA expression profiling was used to characterize developing erythroid cells at six time points during differentiation (days 1, 3, 5, 7, 9, and 11). Eleven thousand seven hundred sixty-three genes (20,963 Affymetrix probe sets) were expressed on day 1, and 1,504 genes, represented by 1,953 probe sets, were differentially expressed (DE) with 537 upregulated and 969 downregulated. A subset of the DE genes was validated using real-time RT-PCR. The DE probe sets were subjected to a cluster metric and could be divided into two, three, four, five, or six clusters of genes with different expression patterns in each cluster. Genes in these clusters were examined for shared transcription factor binding sites (TFBS) in their promoters by comparing enrichment of each TFBS relative to a reference set using transcriptional regulatory network analysis. The sets of TFBS enriched in genes up- and downregulated during erythropoiesis were distinct. This analysis identified transcriptional regulators critical to erythroid development, factors recently found to play a role, as well as a new list of potential candidates, including Evi-1, a potential silencer of genes upregulated during erythropoiesis. Thus this transcriptional regulatory network analysis has yielded a focused set of factors and their target genes whose role in differentiation of the hematopoietic stem cell into distinct blood cell lineages can be elucidated.

Transcriptome Analysis of Human Hematopoietic Progenitor Cells during In Vitro Erythroid Differentiation.

The hematopoietic progenitor cell (HPC) has the potential to differentiate into multiple cell types. Deciphering transcriptional regulatory networks necessary for HPCs to undergo lineage commitment is critical. Toward this goal, we established a serum-free in vitro differentiation system for human HPCs using CD34−positive cells isolated from peripheral blood of normal donors. Cells were cultured in the presence of Epo, SCF and IL-3 for two-weeks, and assessed for total hemoglobin and percent fetal hemoglobin. Adult-derived HPCs express large amounts of Hb A after approximately one week of culture with little expression of Hb F. Transcriptome analysis, using Affymetrix Human Genome U133 Plus 2.0 Arrays on RNA from days 1, 3, 5, 7, 9, 11 and 13 of culture shows more than 1500 differentially-expressed (DE)(> 2-fold up or down) genes, a subset of which were validated independently by real-time RT-PCR. Up-regulated erythroid-specific genes included those for blood group antigens (Kell, Duffy, Rhesus), membrane proteins (ankryin 1, α- and β-spectrin, Band 4.1, Band 4.2, Band 4.9, glycophorin A), receptors (transferrin and erythropoietin receptors), heme biosynthesis enzymes (ALAS, ALAD, UROS, UROD, CPOX, PPOX, FECH), and transcription factors important in globin-gene expression (EKLF, BKLF, NFE2, GATA1) as well as α-, β- and γ-globin mRNAs. Down-regulated genes included those expressed in hematopoietic stem cells (GATA2, CD34) and non-erythroid hematopoietic lineages (MHC class I and II genes, CD33, CD53). The DE genes were clustered into a limited number of expression patterns, suggesting coordinate regulation of genes within each cluster. The DE gene set was examined in silico for co-regulation using transcriptional regulatory network analysis employing PAINT v3.2 software (Vadigepalli R et al., OMICS. 2003; 7(3):235–52). GATA binding sites were represented on 149 of the DE gene promoters. Several transcriptional regulatory elements (TRE) were statistically significantly enriched in the DE gene set when compared to the array gene set. These include TRE for AP-1, CREB, GATA-1 and Oct-1. Co-regulation of genes in the different gene clusters will be analyzed by PAINT and findings validated by examination of TRE:transcription factor interactions. These studies should help define timing of transcription factor occupancy in relation to epigenetic changes occurring prior to transcriptome alteration during differentiation of human erythroid cells.

Analysis of transcription regulatory networks by RNA interference expression analysis

Analysis of transcription regulatory networks by RNA interference expression analysis

Transcriptional Networks and Their Relatives

Living cells are the product of gene expression programs involving regulated transcription of thousands of genes. Lee et al. used immunoprecipitation and microarray studies to build a global analysis of transcriptional regulatory networks in Saccharomyces cerevisiae . Patterns observed were characterized as motifs that were then combined by an automatic process into a network structure. Such transcriptional regulation represents a network of interactions, and similar networks underlie apparently unrelated systems, such as food webs and electronic circuits. To examine their fundamental design features, Milo et al. (see the Perspective by Oltvai and Barabási) looked at local organizing principles of networks, expressed as motifs of how different interactors are joined, that occurred at more than random frequencies. Motifs shared by food webs had features that were not shared by transcription networks from yeast and from Escherichia coli or by electronic circuits, although similarities were seen in information-processing circuits. T. I. Lee, N. J. Rinaldi, F. Robert, D. T. Odom, Z. Bar-Joseph, G. K. Gerber, N. M. Hannett, C. T. Harbison, C. M. Thompson, I. Simon, J. Zeitlinger, E. G. Jennings, H, L. Murray, D. B. Gordon, B. Ren, J. J. Wyrick, J.-B. Tagne, T. L. Volkert, E. Fraenkel, D. K. Gifford, R. A. Young, Transcriptional regulatory networks in Saccharomyces cerevisiae . Science 298 , 799-804 (2002). [Abstract] [Full Text] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, U. Alon, Network motifs: Simple building blocks of complex networks. Science 298 , 824-827 (2002). [Abstract] [Full Text] Z. N. Oltvai, A.-L. Barabási, Life's complexity pyramid. Science 298 , 763-764 (2002). [Summary] [Full Text]