Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org.

An overview of miRNAs altered in Alzheimer's disease (AD) was established by profiling the hippocampus of a cohort of 41 late-onset AD (LOAD) patients and 23 controls, showing deregulation of 35 miRNAs. Profiling of miRNAs in the prefrontal cortex of a second independent cohort of 49 patients grouped by Braak stages revealed 41 deregulated miRNAs. We focused on miR-132-3p which is strongly altered in both brain areas. Downregulation of this miRNA occurs already at Braak stages III and IV, before loss of neuron-specific miRNAs. Next-generation sequencing confirmed a strong decrease of miR-132-3p and of three family-related miRNAs encoded by the same miRNA cluster on chromosome 17. Deregulation of miR-132-3p in AD brain appears to occur mainly in neurons displaying Tau hyper-phosphorylation. We provide evidence that miR-132-3p may contribute to disease progression through aberrant regulation of mRNA targets in the Tau network. The transcription factor (TF) FOXO1a appears to be a key target of miR-132-3p in this pathway.

SUMMARY The expansion of repressive epigenetic marks has been implicated in heterochromatin formation during embryonic development, but the general applicability of this mechanism is unclear. Here we show that nuclear rearrangement of repressive histone marks H3K9me3 and H3K27me3 into nonoverlapping structural layers characterizes senescence-associated heterochromatic foci (SAHF) formation in human fibroblasts. However, the global landscape of these repressive marks remains unchanged upon SAHF formation, suggesting that in somatic cells, heterochromatin can be formed through the spatial repositioning of pre-existing repressively marked histones. This model is reinforced by the correlation of presenescent replication timing with both the subsequent layered structure of SAHFs and the global landscape of the repressive marks, allowing us to integrate microscopic and genomic information. Furthermore, modulation of SAHF structure does not affect the occupancy of these repressive marks, nor vice versa. These experiments reveal that high-order heterochromatin formation and epigenetic remodeling of the genome can be discrete events.

To verify the genome annotation and to create a resource to functionally characterize the proteome, we attempted to Gateway-clone all predicted protein-encoding open reading frames (ORFs), or the 'ORFeome,' of Caenorhabditis elegans. We successfully cloned approximately 12,000 ORFs (ORFeome 1.1), of which roughly 4,000 correspond to genes that are untouched by any cDNA or expressed-sequence tag (EST). More than 50% of predicted genes needed corrections in their intron-exon structures. Notably, approximately 11,000 C. elegans proteins can now be expressed under many conditions and characterized using various high-throughput strategies, including large-scale interactome mapping. We suggest that similar ORFeome projects will be valuable for other organisms, including humans.

The regulatory sequence analysis tools (RSAT, http://rsat.ulb.ac.be/rsat/) is a software suite that integrates a wide collection of modular tools for the detection of cis-regulatory elements in genome sequences. The suite includes programs for sequence retrieval, pattern discovery, phylogenetic footprint detection, pattern matching, genome scanning and feature map drawing. Random controls can be performed with random gene selections or by generating random sequences according to a variety of background models (Bernoulli, Markov). Beyond the original word-based pattern-discovery tools (oligo-analysis and dyad-analysis), we recently added a battery of tools for matrix-based detection of cis-acting elements, with some original features (adaptive background models, Markov-chain estimation of P-values) that do not exist in other matrix-based scanning tools. The web server offers an intuitive interface, where each program can be accessed either separately or connected to the other tools. In addition, the tools are now available as web services, enabling their integration in programmatic workflows. Genomes are regularly updated from various genome repositories (NCBI and EnsEMBL) and 682 organisms are currently supported. Since 1998, the tools have been used by several hundreds of researchers from all over the world. Several predictions made with RSAT were validated experimentally and published.

BackgroundPancreatic cancer is poorly characterized at genetic and non-genetic levels. The current study evaluates in a large cohort of patients the prognostic relevance of molecular subtypes and key transcription factors in pancreatic ductal adenocarcinoma (PDAC).MethodsWe performed gene expression analysis of whole-tumor tissue obtained from 118 surgically resected PDAC and 13 histologically normal pancreatic tissue samples. Cox regression models were used to study the effect on survival of molecular subtypes and 16 clinicopathological prognostic factors. In order to better understand the biology of PDAC we used iRegulon to identify transcription factors (TFs) as master regulators of PDAC and its subtypes.ResultsWe confirmed the PDAssign gene signature as classifier of PDAC in molecular subtypes with prognostic relevance. We found molecular subtypes, but not clinicopathological factors, as independent predictors of survival. Regulatory network analysis predicted that HNF1A/B are among thousand TFs the top enriched master regulators of the genes expressed in the normal pancreatic tissue compared to the PDAC regulatory network. On immunohistochemistry staining of PDAC samples, we observed low expression of HNF1B in well differentiated towards no expression in poorly differentiated PDAC samples. We predicted IRF/STAT, AP-1, and ETS-family members as key transcription factors in gene signatures downstream of mutated KRAS.ConclusionsPDAC can be classified in molecular subtypes that independently predict survival. HNF1A/B seem to be good candidates as master regulators of pancreatic differentiation, which at the protein level loses its expression in malignant ductal cells of the pancreas, suggesting its putative role as tumor suppressor in pancreatic cancer.Trial registrationThe study was registered at ClinicalTrials.gov under the number NCT01116791 (May 3, 2010).Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-016-2540-6) contains supplementary material, which is available to authorized users.

The basic helix-loop-helix transcription factor Scl/Tal1 controls the development and subsequent differentiation of hematopoietic stem cells (HSCs). However, because few Scl target genes have been validated to date, the underlying mechanisms have remained largely unknown. In this study, we have used ChIP-Seq technology (coupling chromatin immunoprecipitation with deep sequencing) to generate a genome-wide catalog of Scl-binding events in a stem/progenitor cell line, followed by validation using primary fetal IntroductionHematopoiesis has long served as a model system for adult stem cells, with many paradigms of stem cell biology first being established as a result of studying hematopoietic stem cells (HSCs). A large body of work over the past 25 years has established that transcription factors (TFs) play critical roles during the specification, maintenance, and/or differentiation of HSCs. However, the underlying mechanisms have remained largely obscure because of a lack of comprehensive data on target genes, as well as very limited information on the way key TFs interact to form the regulatory networks that control blood stem cell development and subsequent behavior.The basic helix-loop-helix (bHLH) TF Scl (also known as Tal1) is required for the specification of HSCs as well as their subsequent differentiation into erythroid and megakaryocytic lineages. 1,2 Sclnull embryos do not survive beyond embryonic day (E) 9.5 due to a complete absence of hematopoiesis, 3,4 a more striking phenotype than seen with other important regulators of early hematopoiesis such as Runx1 or Gata2. [5][6][7] Moreover, together with its paralogue Lyl1, Scl was recently shown to be essential for the survival of adult HSCs, thus emphasizing critical functions for Scl at multiple stages of hematopoietic ontogeny. 8 In addition to its pleiotropic roles in hematopoiesis, Scl is also required for vascular and central nervous system development. [9][10][11] Within the blood system, Scl is thought to be a key component of the regulatory networks controlling the specification and subsequent differentiation of HSCs. 12,13 Studies on the transcriptional regulation of the murine Scl gene identified Ets and Gata factors as well as an autoregulatory loop as key upstream inputs. [14][15][16] However, to fully understand how Scl functions within hematopoietic regulatory networks, comprehensive information on downstream target genes will also be required. Scl has been found to regulate a handful of genes, including Gata1, 17 Runx1, 18 c-kit, 19 and ␣-globin 20 in different hematopoietic lineages. However, to date, no systematic genome-scale approach has been taken to interrogate Scl target genes at early developmental time points in which Scl function is critical.Together with bHLH class I proteins, such as E47, Scl binds DNA as a heterodimer to the so-called E-box sequence motif CANNTG. In addition to its bHLH DNA-binding partners, Scl can interact with various proteins, including the lim-only protein Lmo2 and Gata factors Gata1/Gata2 in multimeric ...

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.