Regulatory Network

The transcription factors OCT4, SOX2, and NANOG have essential roles in early development and are required for the propagation of undifferentiated embryonic stem (ES) cells in culture. To gain insights into transcriptional regulation of human ES cells, we have identified OCT4, SOX2, and NANOG target genes using genome-scale location analysis. We found, surprisingly, that OCT4, SOX2, and NANOG co-occupy a substantial portion of their target genes. These target genes frequently encode transcription factors, many of which are developmentally important homeodomain proteins. Our data also indicate that OCT4, SOX2, and NANOG collaborate to form regulatory circuitry consisting of autoregulatory and feedforward loops. These results provide new insights into the transcriptional regulation of stem cells and reveal how OCT4, SOX2, and NANOG contribute to pluripotency and self-renewal.

Helicobacter pylori, strain 26695, has a circular genome of 1,667,867 base pairs and 1,590 predicted coding sequences. Sequence analysis indicates that H. pylori has well-developed systems for motility, for scavenging iron, and for DNA restriction and modification. Many putative adhesins, lipoproteins and other outer membrane proteins were identified, underscoring the potential complexity of host-pathogen interaction. Based on the large number of sequence-related genes encoding outer membrane proteins and the presence of homopolymeric tracts and dinucleotide repeats in coding sequences, H. pylori, like several other mucosal pathogens, probably uses recombination and slipped-strand mispairing within repeats as mechanisms for antigenic variation and adaptive evolution. Consistent with its restricted niche, H. pylori has a few regulatory networks, and a limited metabolic repertoire and biosynthetic capacity. Its survival in acid conditions depends, in part, on its ability to establish a positive inside-membrane potential in low pH. grant from the National Center for Research Resources. We thank N. S. Akopyants for preparing high quality chromosomal DNA from H. pylori strain 26695; M. Heaney, J. Scott, A. Saeed and R. Shirley for software and database support; and V. Sapiro, B. Vincent, J. Meehan and D. Mass for computer system support.

Background: Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genomewide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by coexpression methods.

Background Jasmonates are ubiquitously occurring lipid-derived compounds with signal functions in plant responses to abiotic and biotic stresses, as well as in plant growth and development. Jasmonic acid and its various metabolites are members of the oxylipin family. Many of ...

The increase of allergic diseases in the industrialized world has often been explained by a decline in infections during childhood. The immunological explanation has been put into the context of the functional T cell subsets known as T helper 1 (T H 1) and T helper 2 (T H 2) that display polarized cytokine profiles. It has been argued that bacterial and viral infections during early life direct the maturing immune system toward T H 1, which counterbalance proallergic responses of T H 2 cells. Thus, a reduction in the overall microbial burden will result in weak T H 1 imprinting and unrestrained T H 2 responses that allow an increase in allergy. This notion is contradicted by observations that the prevalence of T H 1-autoimmune diseases is also increasing and that T H 2-skewed parasitic worm (helminth) infections are not associated with allergy. More recently, elevations of anti-inflammatory cytokines, such as interleukin-10, that occur during long-term helminth infections have been shown to be inversely correlated with allergy. The induction of a robust anti-inflammatory regulatory network by persistent immune challenge offers a unifying explanation for the observed inverse association of many infections with allergic disorders.

The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.

npg DNA double-strand breaks (DSBs) are critical lesions that can result in cell death or a wide variety of genetic alterations including large-or small-scale deletions, loss of heterozygosity, translocations, and chromosome loss. DSBs are repaired by non-homologous end-joining (NHEJ) and homologous recombination (HR), and defects in these pathways cause genome instability and promote tumorigenesis. DSBs arise from endogenous sources including reactive oxygen species generated during cellular metabolism, collapsed replication forks, and nucleases, and from exogenous sources including ionizing radiation and chemicals that directly or indirectly damage DNA and are commonly used in cancer therapy. The DSB repair pathways appear to compete for DSBs, but the balance between them differs widely among species, between different cell types of a single species, and during different cell cycle phases of a single cell type. Here we review the regulatory factors that regulate DSB repair by NHEJ and HR in yeast and higher eukaryotes. These factors include regulated expression and phosphorylation of repair proteins, chromatin modulation of repair factor accessibility, and the availability of homologous repair templates. While most DSB repair proteins appear to function exclusively in NHEJ or HR, a number of proteins influence both pathways, including the MRE11/RAD50/NBS1(XRS2) complex, BRCA1, histone H2AX, PARP-1, RAD18, DNA-dependent protein kinase catalytic subunit (DNA-PKcs), and ATM. DNA-PKcs plays a role in mammalian NHEJ, but it also influences HR through a complex regulatory network that may involve crosstalk with ATM, and the regulation of at least 12 proteins involved in HR that are phosphorylated by DNA-PKcs and/or ATM.

In plants, microRNAs (miRNAs) comprise one of two classes of small RNAs that function primarily as negative regulators at the posttranscriptional level. Several MIRNA genes in the plant kingdom are ancient, with conservation extending between angiosperms and the mosses, whereas many others are more recently evolved. Here, we use deep sequencing and computational methods to identify, profile and analyze non-conserved MIRNA genes in Arabidopsis thaliana. 48 nonconserved MIRNA families, nearly all of which were represented by single genes, were identified. Sequence similarity analyses of miRNA precursor foldback arms revealed evidence for recent evolutionary origin of 16 MIRNA loci through inverted duplication events from protein-coding gene sequences. Interestingly, these recently evolved MIRNA genes have taken distinct paths. Whereas some non-conserved miRNAs interact with and regulate target transcripts from gene families that donated parental sequences, others have drifted to the point of non-interaction with parental gene family transcripts. Some young MIRNA loci clearly originated from one gene family but form miRNAs that target transcripts in another family. We suggest that MIRNA genes are undergoing relatively frequent birth and death, with only a subset being stabilized by integration into regulatory networks.

Infected plants undergo transcriptional reprogramming during initiation of both local defence and systemic acquired resistance (SAR). We monitored gene-expression changes in Arabidopsis thaliana under 14 different SAR-inducing or SAR-repressing conditions using a DNA microarray representing approximately 25-30% of all A. thaliana genes. We derived groups of genes with common regulation patterns, or regulons. The regulon containing PR-1, a reliable marker gene for SAR in A. thaliana, contains known PR genes and novel genes likely to function during SAR and disease resistance. We identified a common promoter element in genes of this regulon that binds members of a plant-specific transcription factor family. Our results extend expression profiling to definition of regulatory networks and gene discovery in plants.

The interactions between proteins, DNA, and RNA in living cells constitute molecular networks that govern various cellular functions. To investigate the global dynamical properties and stabilities of such networks, we studied the cell-cycle regulatory network of the budding yeast. With the use of a simple dynamical model, it was demonstrated that the cell-cycle network is extremely stable and robust for its function. The biological stationary state, the G1 state, is a global attractor of the dynamics. The biological pathway, the cell-cycle sequence of protein states, is a globally attracting trajectory of the dynamics. These properties are largely preserved with respect to small perturbations to the network. These results suggest that cellular regulatory networks are robustly designed for their functions.

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.

The inference of transcriptional networks that regulate transitions into physiological or pathological cellular states remains a central challenge in systems biology. A mesenchymal phenotype is the hallmark of tumour aggressiveness in human malignant glioma, but the regulatory programs responsible for implementing the associated molecular signature are largely unknown. Here we show that reverse-engineering and an unbiased interrogation of a glioma-specific regulatory network reveal the transcriptional module that activates expression of mesenchymal genes in malignant glioma. Two transcription factors (C/EBPβ and STAT3) emerge as synergistic initiators and master regulators of mesenchymal transformation. Ectopic co-expression of C/EBPβ and STAT3 reprograms neural stem cells along the aberrant mesenchymal lineage, whereas elimination of the two factors in glioma cells leads to collapse of the mesenchymal signature and reduces tumour aggressiveness. In human glioma, expression of C/EBPβ and STAT3 correlates with mesenchymal differentiation and predicts poor clinical outcome. These results show that the activation of a small regulatory module is necessary and sufficient to initiate and maintain an aberrant phenotypic state in cancer cells.

A major component in the regulatory network controlling fruit ripening is likely to be the gene at the tomato Colorless nonripening (Cnr) locus 1,2 . The Cnr mutation results in colorless fruits with a substantial loss of cell-to-cell adhesion. The nature of the mutation and the identity of the Cnr gene were previously unknown. Using positional cloning and virusinduced gene silencing, here we demonstrate that an SBP-box (SQUAMOSA promoter binding protein-like) gene resides at the Cnr locus. Furthermore, the Cnr phenotype results from a spontaneous epigenetic change in the SBP-box promoter. The discovery that Cnr is an epimutation was unexpected, as very few spontaneous epimutations have been described in plants 3,4 . This study demonstrates that an SBP-box gene is critical for normal ripening and highlights the likely importance of epialleles in plant development and the generation of natural variation.

Ghrelin, a recently described endogenous ligand for the growth hormone secretagogue receptor (GHS-R), is produced by stomach cells and is a potent circulating orexigen, controlling energy expenditure, adiposity, and growth hormone secretion. However, the functional role of ghrelin in regulation of immune responses remains undefined. Here we report that GHS-R and ghrelin are expressed in human T lymphocytes and monocytes, where ghrelin acts via GHS-R to specifically inhibit the expression of proinflammatory anorectic cytokines such as IL-1β, IL-6, and TNF-α. Ghrelin led to a dose-dependent inhibition of leptin-induced cytokine expression, while leptin upregulated GHS-R expression on human T lymphocytes. These data suggest the existence of a reciprocal regulatory network by which ghrelin and leptin control immune cell activation and inflammation. Moreover, ghrelin also exerts potent anti-inflammatory effects and attenuates endotoxin-induced anorexia in a murine endotoxemia model. We believe this to be the first report demonstrating that ghrelin functions as a key signal, coupling the metabolic axis to the immune system, and supporting the potential use of ghrelin and GHS-R agonists in the management of disease-associated cachexia. Nonstandard abbreviations used: Alexa Fluor (AF); G protein-coupled receptor (GPCR); growth hormone (GH); growth hormone secretagogue receptor (GHS-R); neuropeptide Y (NPY); stromal cell-derived factor-1α (SDF-1α); T cell receptor (TCR).

The genomic regulatory network that controls gene expression ultimately determines form and function in each species. The operational nature of the regulatory programming specified in cis-regulatory DNA sequence was determined from a detailed functional analysis of a sea urchin control element that directs the expression of a gene in the endoderm during development. Spatial expression and repression, and the changing rate of transcription of this gene, are mediated by a complex and extended cis-regulatory system. The system may be typical of developmental cis-regulatory apparatus. All of its activities are integrated in the proximal element, which contains seven target sites for DNA binding proteins. A quantitative computational model of this regulatory element was constructed that explicitly reveals the logical interrelations hard-wired into the DNA.

The TRANSFAC database on transcription factors and their DNA-binding sites and profiles (http:// www.gene-regulation.de/) has been quantitatively extended and supplemented by a number of modules. These modules give information about pathologically relevant mutations in regulatory regions and transcription factor genes (PathoDB), scaffold/matrix attached regions (S/MARt DB), signal transduction (TRANSPATH) and gene expression sources (CYTOMER). Altogether, these distinct database modules constitute the TRANSFAC system. They are accompanied by a number of program routines for identifying potential transcription factor binding sites or for localizing individual components in the regulatory network of a cell.

Plants continuously maintain pools of totipotent stem cells in their apical meristems from which elaborate root and shoot systems are produced. In Arabidopsis thaliana, stem cell fate in the shoot apical meristem is controlled by a regulatory network that includes the CLAVATA (CLV) ligand-receptor system and the homeodomain protein WUSCHEL (WUS) 1,2 . Phytohormones such as auxin and cytokinin are also important for meristem regulation 3 . Here we show a mechanistic link between the CLV/WUS network and hormonal control. WUS, a positive regulator of stem cells, directly represses the transcription of several two-component ARABIDOPSIS RESPONSE REGULATOR genes (ARR5, ARR6, ARR7 and ARR15), which act in the negativefeedback loop of cytokinin signalling 4,5 . These data indicate that ARR genes might negatively influence meristem size and that their repression by WUS might be necessary for proper meristem function. Consistent with this hypothesis is our observation that a mutant ARR7 allele, which mimics the active, phosphorylated form, causes the formation of aberrant shoot apical meristems. Conversely, a loss-of-function mutation in a maize ARR homologue was recently shown to cause enlarged meristems 6 .

the end of M and early G1. It is not yet clear whether this model, developed using a small set of genes, will Nine Cambridge Center Cambridge, Massachusetts 02142 extrapolate to regulation of all cell cycle genes. Microarray analysis has revealed that the expression levels of approximately 800 genes vary in a periodic fashion during the yeast cell cycle (Cho et al., 1998; Spellman et al., 1998), but little is known about the regulation of most of these genes. The set of genes controlled 200 Technology Square Cambridge, Massachusetts 02139 by MBF and SBF has recently been identified by using a genome-wide binding method, confirming that these factors are largely bound to genes expressed in late G1 and revealing how sets of functionally related genes are Summary regulated during this time (Iyer et al., 2001). Identification of the genes regulated by all nine transcription factors Genome-wide location analysis was used to determine how the yeast cell cycle gene expression program is

Nature 451, 437-440 (2008) In , the descriptions of the continuous and dotted curves were inadvertently swapped. The continuous line corresponds to the modified synthetic thermal profile (storms reaching the 60 mbar level). The dotted line corresponds to the Cassini CIRS thermal profile (storms reaching the 160 mbar level).

Drought stress is one of the major limitations to crop productivity. To develop crop plants with enhanced tolerance of drought stress, a basic understanding of physiological, biochemical and gene regulatory networks is essential. Various functional genomics tools have helped to advance our understanding of stress signal perception and transduction, and of the associated molecular regulatory network. These tools have revealed several stress-inducible genes and various transcription factors that regulate the drought-stress-inducible systems. Translational genomics of these candidate genes using model plants provided encouraging results, but the field testing of transgenic crop plants for better performance and yield is still minimal. Better understanding of the specific roles of various metabolites in crop stress tolerance will give rise to a strategy for the metabolic engineering of crop tolerance of drought.

The gonadotropic axis is centrally controlled by a complex regulatory network of excitatory and inhibitory signals that is activated at puberty. Recently, loss of function mutations of the gene encoding G protein-coupled receptor 54 (GPR54), the putative receptor for the KiSS-1-derived peptide metastin, have been associated with lack of puberty onset and hypogonadotropic hypogonadism. Yet the pattern of expression and functional role of the KiSS-1/GPR54 system in the rat hypothalamus remain unexplored to date. In the present work, expression analyses of KiSS-1 and GPR54 genes were conducted in different physiological and experimental settings, and the effects of central administration of KiSS-1 peptide on LH release were assessed in vivo. Persistent expression of KiSS-1 and GPR54 mRNAs was detected in rat hypothalamus throughout postnatal development, with maximum expression levels at puberty in both male and female rats. Hypothalamic expression of KiSS-1 and GPR54 genes changed throughout the estrous cycle and was significantly increased after gonadectomy, a rise that was prevented by sex steroid replacement both in males and females. Moreover, hypothalamic expression of the KiSS-1 gene was sensitive to neonatal imprinting by estrogen. From a functional standpoint, intracerebroventricular administration of KiSS-1 peptide induced a dramatic increase in serum LH levels in prepubertal male and female rats as well as in adult animals. In conclusion, we provide novel evidence of the developmental and hormonally regulated expression of KiSS-1 and GPR54 mRNAs in rat hypothalamus and the ability of KiSS-1 peptide to potently stimulate LH secretion in vivo. Our current data support the contention that the hypothalamic KiSS-1/GPR54 system is a pivotal factor in central regulation of the gonadotropic axis at puberty and in adulthood. (Endocrinology 145: [4565][4566][4567][4568][4569][4570][4571][4572][4573][4574] 2004) T HE PITUITARY GONADOTROPINS, LH and FSH, are

We describe an algorithm for discovering regulatory networks of gene modules, GRAM (Genetic Regulatory Modules), that combines information from genome-wide location and expression data sets. A gene module is defined as a set of coexpressed genes to which the same set of transcription factors binds. Unlike previous approaches 1-5 that relied primarily on functional information from expression data, the GRAM algorithm explicitly links genes to the factors that regulate them by incorporating DNA binding data, which provide direct physical evidence of regulatory interactions. We use the GRAM algorithm to describe a genome-wide regulatory network in Saccharomyces cerevisiae using binding information for 106 transcription factors profiled in rich medium conditions and data from over 500 expression experiments. We also present a genome-wide location analysis data set for regulators in yeast cells treated with rapamycin, and use the GRAM algorithm to provide biological insights into this regulatory network.

The haploid male gametophyte generation of flowering plants consists of two-or three-celled pollen grains. This functional specialization is thought to be a key factor in the evolutionary success of flowering plants. Moreover, pollen ontogeny is also an attractive model in which to dissect cellular networks that control cell growth, asymmetric cell division and cellular differentiation. Our objective, and an essential step towards the detailed understanding of these processes, was to comprehensively define the male haploid transcriptome throughout development.

Motivation: Signaling pathways are dynamic events that take place over a given period of time. In order to identify these pathways, expression data over time are required. Dynamic Bayesian network (DBN) is an important approach for predicting the gene regulatory networks from time course expression data. However, two fundamental problems greatly reduce the effectiveness of current DBN methods. The first problem is the relatively low accuracy of prediction, and the second is the excessive computational time. Results: In this paper, we present a DBN-based approach with increased accuracy and reduced computational time compared with existing DBN methods. Unlike previous methods, our approach limits potential regulators to those genes with either earlier or simultaneous expression changes (up-or down-regulation) in relation to their target genes. This allows us to limit the number of potential regulators and consequently reduce the search space. Furthermore, we use the time difference between the initial change in the expression of a given regulator gene and its potential target gene to estimate the transcriptional time lag between these two genes. This method of time lag estimation increases the accuracy of predicting gene regulatory networks. Our approach is evaluated using time-series expression data measured during the yeast cell cycle. The results demonstrate that this approach can predict regulatory networks with significantly improved accuracy and reduced computational time compared with existing DBN approaches.

Eventually to understand the integrated function of the cell cycle regulatory network, we must organize the known interactions in the form of a diagram, map, and/or database. A diagram convention was designed capable of unambiguous representation of networks containing multiprotein complexes, protein modifications, and enzymes that are substrates of other enzymes. To facilitate linkage to a database, each molecular species is symbolically represented only once in each diagram. Molecular species can be located on the map by means of indexed grid coordinates. Each interaction is referenced to an annotation list where pertinent information and references can be found. Parts of the network are grouped into functional subsystems. The map shows how multiprotein complexes could assemble and function at gene promoter sites and at sites of DNA damage. It also portrays the richness of connections between the p53-Mdm2 subsystem and other parts of the network.

Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or clusters of genes with common activity profiles may overlook the most critical features of cellular information processing which normally determine how signal specificity is established and maintained in living cells.

Motivation: To understand the behaviour of complex biological regulatory networks, a proper integration of molecular data into a full-fledge formal dynamical model is ultimately required. As most available data on regulatory interactions are qualitative, logical modelling offers an interesting framework to delineate the main dynamical properties of the underlying networks. Results: Transposing a generic model of the core network controlling the mammalian cell cycle into the logical framework, we compare different strategies to explore its dynamical properties. In particular, we assess the respective advantages and limits of synchronous versus asynchronous updating assumptions to delineate the asymptotical behaviour of regulatory networks. Furthermore, we propose several intermediate strategies to optimize the computation of asymptotical properties depending on available knowledge. Availability: The mammalian cell cycle model is available in a dedicated XML format (GINML) on our website, along with our logical simulation software GINsim (http://gin.univ-mrs.fr/GINsim). Higher resolution state transitions graphs are also found on this web site (Model Repository page).

The transcription factor IRF4 is required during an immune response for lymphocyte activation and the generation of immunoglobulin-secreting plasma cells 1-3 . Multiple myeloma, a malignancy of plasma cells, has a complex molecular etiology with several subgroups defined by gene expression profiling and recurrent chromosomal translocations 4,5 . Moreover, the malignant clone can sustain multiple oncogenic lesions, accumulating genetic damage as the disease progresses 6,7 . Current therapies for myeloma can extend survival but are not curative 8,9 . Hence, new therapeutic strategies are needed that target molecular pathways shared by all subtypes of myeloma. Using a loss-offunction, RNA-interference-based genetic screen we show here that IRF4 inhibition was toxic to myeloma cell lines, regardless of transforming oncogenic mechanism. Gene expression profiling and genome-wide chromatin immunoprecipitation analysis uncovered an extensive network of IRF4 target genes and identified MYC as a direct target of IRF4 in activated B cells and myeloma. Unexpectedly, IRF4 was itself a direct target of MYC transactivation, generating an autoregulatory circuit in myeloma cells. Though IRF4 is not genetically altered in most myelomas, they are nonetheless addicted to an aberrant IRF4 regulatory network that fuses the gene expression programs of normal plasma cells and activated B cells.

The TP53 gene has a prominent role in cancer and much of human biology. The 'guardian of the genome' continues to fascinate investigators because of its many functions. The p53 tumour suppressor can be induced by a range of stresses through transcriptional 1,2 , posttranscriptional 3-8 and post-translational 9 control mechanisms. The intrigue caused by this protein has been heightened by recent findings that p53 activities also occur in human development long before the onset of cancer, including during embryo implantation 10 . Many functions have been attributed to p53, including direct roles in repair and recombination, association with proteins involved in genome stability, and chromatin modification 11 . However, its broadest cellular effect is that of a transcription factor (TF) 12 . In its role as a master regulator, the universe of genes subject to p53 control extends across a diverse group of biological activities 13 that include DNA metabolism 11 , apoptosis 14 , cell cycle regulation 15 , senescence 16 , energy metabolism 17,18 , angiogenesis 19-22 , immune response 23 , cell differentiation, motility and migration 24-28 and cell-cell communication . Recent studies have demonstrated how p53-dependent activation of microRNA genes can participate in the modulation of various biological activities . Approximately 50% of all cancers have a mutation in p53 that alters transcriptional activity (see the International Agency for Cancer Research TP53 Mutation Database and the TP53 Website). In most of the remaining cancers, the functions of the p53 pathway are impaired mainly through a reduction in nuclear p53 levels, resulting in multiple changes in stress responses and cellular fate.

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified.

microRNAs (miRs) are small RNAs that regulate gene expression at the posttranscriptional level. It is anticipated that, in combination with transcription factors (TFs), they span a regulatory network that controls thousands of mammalian genes. Here we set out to uncover local and global architectural features of the mammalian miR regulatory network. Using evolutionarily conserved potential binding sites of miRs in human targets, and conserved binding sites of TFs in promoters, we uncovered two regulation networks. The first depicts combinatorial interactions between pairs of miRs with many shared targets. The network reveals several levels of hierarchy, whereby a few miRs interact with many other lowly connected miR partners. We revealed hundreds of ''target hubs'' genes, each potentially subject to massive regulation by dozens of miRs. Interestingly, many of these target hub genes are transcription regulators and they are often related to various developmental processes. The second network consists of miR-TF pairs that coregulate large sets of common targets. We discovered that the network consists of several recurring motifs. Most notably, in a significant fraction of the miR-TF coregulators the TF appears to regulate the miR, or to be regulated by the miR, forming a diversity of feed-forward loops. Together these findings provide new insights on the architecture of the combined transcriptional-post transcriptional regulatory network.

A comparative transcriptome analysis for successive stages of Arabidopsis (Arabidopsis thaliana) developmental leaf senescence (NS), darkening-induced senescence of individual leaves attached to the plant (DIS), and senescence in dark-incubated detached leaves (DET) revealed many novel senescence-associated genes with distinct expression profiles. The three senescence processes share a high number of regulated genes, although the overall number of regulated genes during DIS and DET is about 2 times lower than during NS. Consequently, the number of NS-specific genes is much higher than the number of DIS-or DET-specific genes. The expression profiles of transporters (TPs), receptor-like kinases, autophagy genes, and hormone pathways were analyzed in detail. The Arabidopsis TPs and other integral membrane proteins were systematically reclassified based on the Transporter Classification system. Coordinate activation or inactivation of several genes is observed in some TP families in all three or only in individual senescence types, indicating differences in the genetic programs for remobilization of catabolites. Characteristic senescence type-specific differences were also apparent in the expression profiles of (putative) signaling kinases. For eight hormones, the expression of biosynthesis, metabolism, signaling, and (partially) response genes was investigated. In most pathways, novel senescence-associated genes were identified. The expression profiles of hormone homeostasis and signaling genes reveal additional players in the senescence regulatory network.

The hypothalamic-pituitary-adrenal (HPA) axis is the major endocrine stress axis of the human organism. Cortisol, the final hormone of this axis, affects metabolic, cardiovascular and central nervous systems both acutely and chronically. Recent advances in neuroimaging techniques have led to the investigation of regulatory networks and mechanisms of cortisol regulation in the central nervous system in human populations. In the following review, results from human and animal studies are being presented that investigate the specific role of hippocampus (HC), amygdala (AG), prefrontal cortex (PFC), and brainstem nuclei in cortisol regulation in response to stress. In general, the types of stressors need to be distinguished when discussing the contributions of these structures in regulating the HPA axis. We propose a basic framework on how these structures communicate as a network to regulate cortisol secretion in response to psychological stress. Furthermore, we review critical studies that have substantially contributed to the literature. Possible future research avenues in the field of neuroimaging of cortisol regulation are discussed. In combination with investigations on genetic and environmental factors that influence the development of the HPA axis, this emerging new research will eventually allow the formulation of a more comprehensive framework of functional neuroanatomy of cortisol regulation.

EcoCyc (http://EcoCyc.org) provides a comprehensive encyclopedia of Escherichia coli biology. EcoCyc integrates information about the genome, genes and gene products; the metabolic network; and the regulatory network of E. coli. Recent EcoCyc developments include a new initiative to represent and curate all types of E. coli regulatory processes such as attenuation and regulation by small RNAs. EcoCyc has started to curate Gene Ontology (GO) terms for E. coli and has made a dataset of E. coli GO terms available through the GO Web site. The curation and visualization of electron transfer processes has been significantly improved. Other software and Web site enhancements include the addition of tracks to the EcoCyc genome browser, in particular a type of track designed for the display of ChIP-chip datasets, and the development of a comparative genome browser. A new Genome Omics Viewer enables users to paint omics datasets onto the full E. coli genome for analysis. A new advanced query page guides users in interactively constructing complex database queries against EcoCyc. A Macintosh

Models of mammalian regulatory networks controlling gene expression have been inferred from genomic data, yet have largely not been validated. We present an unbiased strategy to systematically perturb candidate regulators and monitor cellular transcriptional responses. We apply this approach to derive regulatory networks that control the transcriptional response of mouse primary dendritic cells (DCs) to pathogens. Our approach revealed the regulatory functions of 125 transcription factors, chromatin modifiers, and RNA binding proteins and constructed a network model consisting of two dozen core regulators and 76 fine-tuners that help explain how pathogen-sensing pathways achieve specificity. This study establishes a broadly-applicable, comprehensive and unbiased approach to reveal the wiring and functions of a regulatory network controlling a major transcriptional response in primary mammalian cells.

Natural habitats of some microorganisms may fluctuate erratically, whereas others, which are more predictable, offer the opportunity to prepare in advance for the next environmental change. In analogy to classical Pavlovian conditioning, microorganisms may have evolved to anticipate environmental stimuli by adapting to their temporal order of appearance.

The heart develops from two bilateral heart fields that are formed during early gastrulation. In recent years, signaling pathways that specify cardiac mesoderm have been extensively analyzed. In addition, a battery of transcription factors that regulate different aspects of cardiac morphogenesis and cytodifferentiation have been identified and characterized in model organisms. At the anterior pole, a secondary heart field is formed, which in its molecular make-up, appears to be similar to the primary heart field. The cardiac outflow tract and the right ventricle to a large extent are derivatives of this anterior heart field. Cardiac mesoderm receives positional information by which it is patterned along the three body axes. The molecular control of left-right axis development has received particular attention, and the underlying regulatory network begins to emerge. Cardiac chamber development involves the activation of a transcription program that is different from the one present in the primary heart field and regulates cardiac morphogenesis in a region-specific manner. This review also attempts to identify areas in which additional research is needed to fully understand early cardiac development.

Rising demand for food and bioenergy makes it imperative to breed for increased crop yield. Vegetative plant growth could be driven by resource acquisition or developmental programs. Metabolite profiling in 94 Arabidopsis accessions revealed that biomass correlates negatively with many metabolites, especially starch. Starch accumulates in the light and is degraded at night to provide a sustained supply of carbon

Plants adapt to different environmental conditions by constantly forming new organs in response to morphogenetic signals. Lateral roots branch from the main root in response to local auxin maxima. How a local auxin maximum translates into a robust pattern of gene activation ensuring the proper growth of the newly formed lateral root is largely unknown. Here, we demonstrate that miR390, TAS3-derived trans-acting short-interfering RNAs (tasiRNAs), and AUXIN RESPONSE FACTORS (ARFs) form an auxin-responsive regulatory network controlling lateral root growth. Spatial expression analysis using reporter gene fusions, tasi/miRNA sensors, and mutant analysis showed that miR390 is specifically expressed at the sites of lateral root initiation where it triggers the biogenesis of tasiRNAs. These tasiRNAs inhibit ARF2, ARF3, and ARF4, thus releasing repression of lateral root growth. In addition, ARF2, ARF3, and ARF4 affect auxin-induced miR390 accumulation. Positive and negative feedback regulation of miR390 by ARF2, ARF3, and ARF4 thus ensures the proper definition of the miR390 expression pattern. This regulatory network maintains ARF expression in a concentration range optimal for specifying the timing of lateral root growth, a function similar to its activity during leaf development. These results also show how small regulatory RNAs integrate with auxin signaling to quantitatively regulate organ growth during development.

Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.

MIKC-type proteins represent a class of MADS-domain transcription factors and are defined by a unique domain structure: in addition to the highly conserved DNA-binding MADS-domain, they have three other domains (dIT, dKT and dCT), with the keratin-like K-domain being the most highly conserved and characteristic one. The number and functional diversity of MIKC-type proteins increased considerably during land plant evolution, culminating in higher flowering plants, where they dominate the control of reproductive development from early to late stages. We wonder how one special class of proteins became important in the control of essentially all stages of a morphogenetic process. All MADS-domain proteins appear to bind to DNA as homo-or heterodimers and may function as part of ternary transcription factor complexes involving non-MADS-domain proteins. Only MIKC-type proteins, however, generate complex intrafamily interaction networks. These are based on the special potential of MIKC-type proteins to form complexes involving more than two homologous proteins constituting transcriptional regulators. We speculate that the potential to form heteromultimers of homologous proteins was achieved by the acquisition of the K-domain during evolution. There is emerging evidence that organismal complexity arises from progressively more elaborate regulation of gene expression. We hypothesize that combinatorial multimer formation of MIKC-type MADS-domain proteins facilitated an unusually efficient and rapid functional diversification based on gene duplication, sequence divergence and fixation. This dnetworkingT may have enabled a more sophisticated transcriptional control of target genes which was recruited for controlling increasingly complex and diverse developmental pathways during the rapid origin and diversification of plant reproductive structures. Therefore, MIKC-type proteins may owe their evolutionary dsuccessT and present-day developmental importance in part to their modular domain structure. Investigating the evolution of MIKC-type genes may thus help to better understand origin and diversification of gene regulatory networks. D

Structural features found in biomolecular networks that are absent in random networks produced by simple algorithms can provide insight into the function and evolution of cell regulatory networks. Here we analyze "betweenness" of network nodes, a graph theoretical centrality measure, in the yeast protein interaction network. Proteins that have high betweenness, but low connectivity (degree), were found to be abundant in the yeast proteome. This finding is not explained by algorithms proposed to explain the scale-free property of protein interaction networks, where low-connectivity proteins also have low betweenness. These data suggest the existence of some modular organization of the network, and that the high-betweenness, low-connectivity proteins may act as important links between these modules. We found that proteins with high betweenness are more likely to be essential and that evolutionary age of proteins is positively correlated with betweenness. By comparing different models of genome evolution that generate scale-free networks, we show that rewiring of interactions via mutation is an important factor in the production of such proteins. The evolutionary and functional significance of these observations are discussed.

Background Structural analysis of cellular interaction networks contributes to a deeper understanding of network-wide interdependencies, causal relationships, and basic functional capabilities. While the structural analysis of metabolic networks is a well-established field, similar methodologies have been scarcely developed and applied to signaling and regulatory networks. Results We propose formalisms and methods, relying on adapted and partially newly introduced approaches, which facilitate a structural analysis of signaling and regulatory networks with focus on functional aspects. We use two different formalisms to represent and analyze interaction networks: interaction graphs and (logical) interaction hypergraphs. We show that, in interaction graphs, the determination of feedback cycles and of all the signaling paths between any pair of species is equivalent to the computation of elementary modes known from metabolic networks. Knowledge on the set of signaling paths and feedback loops facilitates the computation of intervention strategies and the classification of compounds into activators, inhibitors, ambivalent factors, and non-affecting factors with respect to a certain species. In some cases, qualitative effects induced by perturbations can be unambiguously predicted from the network scheme. Interaction graphs however, are not able to capture AND relationships which do frequently occur in interaction networks. The consequent logical concatenation of all the arcs pointing into a species leads to Boolean networks. For a Boolean representation of cellular interaction networks we propose a formalism based on logical (or signed) interaction hypergraphs, which facilitates in particular a logical steady state analysis (LSSA). LSSA enables studies on the logical processing of signals and the identification of optimal intervention points (targets) in cellular networks. LSSA also reveals network regions whose parametrization and initial states are crucial for the dynamic behavior. We have implemented these methods in our software tool CellNetAnalyzer (successor of FluxAnalyzer) and illustrate their applicability using a logical model of T-Cell receptor signaling providing non-intuitive results regarding feedback loops, essential elements, and (logical) signal processing upon different stimuli. Conclusion The methods and formalisms we propose herein are another step towards the comprehensive functional analysis of cellular interaction networks. Their potential, shown on a realistic T-cell signaling model, makes them a promising tool.

The virulence of Staphylococcus aureus is essentially determined by cell wall associated proteins and secreted toxins that are regulated and expressed according to growth phases and/or growth conditions. Gene expression is regulated by specific and sensitive mechanisms, most of which act at the transcriptional level. Regulatory factors constitute numerous complex networks, driving specific interactions with target gene promoters. These factors are largely regulated by two-component regulatory systems, such as the agr, saeRS, srrAB, arlSR and lytRS systems. These systems are sensitive to environmental signals and consist of a sensor histidine kinase and a response regulator protein. DNA-binding proteins, such as SarA and the recently identified SarA homologues (SarR, Rot, SarS, SarT, SarU), also regulate virulence factor expression. These homologues might be intermediates in the regulatory networks. The multiple pathways generated by these factors allow the bacterium to adapt to environmental conditions rapidly and specifically, and to develop infection. Precise knowledge of these regulatory mechanisms and how they control virulence factor expression would open up new perspectives for antimicrobial chemotherapy using key inhibitors of these systems.