CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse

<p>Abstract</p> <p>Background</p> <p>Transcription Factors (TFs) and microRNAs (miRNAs) are key players for gene expression regulation in higher eukaryotes. In the last years, a large amount of bioinformatic studies were devoted to the elucidation of transcriptional and post-transcriptional (mostly miRNA-mediated) regulatory interactions, but little is known about the interplay between them.</p> <p>Description</p> <p>Here we describe a dynamic web-accessible database, <monospace>CircuitsDB</monospace>, supporting a genome-wide transcriptional and post-transcriptional regulatory network integration, for the human and mouse genomes, based on a bioinformatic sequence-analysis approach. In particular, <monospace>CircuitsDB</monospace> is currently focused on the study of mixed miRNA/TF Feed-Forward regulatory Loops (FFLs), i.e. elementary circuits in which a master TF regulates an miRNA and together with it a set of Joint Target protein-coding genes. The database was constructed using an ab-initio oligo analysis procedure for the identification of the transcriptional and post-transcriptional interactions. Several external sources of information were then pooled together to obtain the functional annotation of the proposed interactions. Results for human and mouse genomes are presented in an integrated web tool, that allows users to explore the circuits, investigate their sequence and functional properties and thus suggest possible biological experiments.</p> <p>Conclusions</p> <p>We present <monospace>CircuitsDB</monospace>, a web-server devoted to the study of human and mouse mixed miRNA/TF Feed-Forward regulatory circuits, freely available at: <url>http://biocluster.di.unito.it/circuits/</url></p

Single-cell states in the estrogen response of breast cancer cell lines

Estrogen responsive breast cancer cell lines have been extensively studied to characterize transcriptional patterns in hormone-responsive tumors. Nevertheless, due to current technological limitations, genome-wide studies have typically been limited to population averaged data. Here we obtain, for the first time, a characterization at the single-cell level of the states and expression signatures of a hormone-starved MCF-7 cell system responding to estrogen. To do so, we employ a recently proposed model that allows for dissecting single-cell states from time-course microarray data. We show that within 32 hours following stimulation, MCF-7 cells traverse, most likely, six states, with a faster early response followed by a progressive deceleration. We also derive the genome-wide transcriptional profiles of such single-cell states and their functional characterization. Our results support a scenario where estrogen promotes cell cycle progression by controlling multiple, sequential regulatory steps, whose single-cell events are here identified. © 2014 Casale et al

Genome-Wide Survey of MicroRNA - Transcription Factor Feed-Forward Regulatory Circuits in Human

In this work, we describe a computational framework for the genome-wide identification and characterization of mixed transcriptional/post-transcriptional regulatory circuits in humans. We concentrated in particular on feed-forward loops (FFL), in which a master transcription factor regulates a microRNA, and together with it, a set of joint target protein coding genes. The circuits were assembled with a two step procedure. We first constructed separately the transcriptional and post-transcriptional components of the human regulatory network by looking for conserved over-represented motifs in human and mouse promoters, and 3'-UTRs. Then, we combined the two subnetworks looking for mixed feed-forward regulatory interactions, finding a total of 638 putative (merged) FFLs. In order to investigate their biological relevance, we filtered these circuits using three selection criteria: (I) GeneOntology enrichment among the joint targets of the FFL, (II) independent computational evidence for the regulatory interactions of the FFL, extracted from external databases, and (III) relevance of the FFL in cancer. Most of the selected FFLs seem to be involved in various aspects of organism development and differentiation. We finally discuss a few of the most interesting cases in detail.Comment: 51 pages, 5 figures, 4 tables. Supporting information included. Accepted for publication in Molecular BioSystem

Transcriptional Hallmarks of Noonan Syndrome and Noonan-Like Syndrome with Loose Anagen Hair

Noonan syndrome (NS) is among the most common nonchromosomal disorders affecting development and growth. NS is genetically heterogeneous, being caused by germline mutations affecting various genes implicated in the RAS signaling network. This network transduces extracellular signals into intracellular biochemical and transcriptional responses controlling cell proliferation, differentiation, metabolism, and senescence. To explore the transcriptional consequences of NS-causing mutations, we performed global mRNA expression profiling on peripheral blood mononuclear cells obtained from 23 NS patients carrying heterozygous mutations in PTPN11 or SOS1. Gene expression profiling was also resolved in five subjects with Noonan-like syndrome with loose anagen hair (NS/LAH), a condition clinically related to NS and caused by an invariant mutation in SHOC2. Robust transcriptional signatures were found to specifically discriminate each of the three mutation groups from 21 age- and sex-matched controls. Despite the only partial overlap in terms of gene composition, the three signatures showed a notable concordance in terms of biological processes and regulatory circuits affected. These data establish expression profiling of peripheral blood mononuclear cells as a powerful tool to appreciate differential perturbations driven by germline mutations of transducers involved in RAS signaling and to dissect molecular mechanisms underlying NS and other RASopathies. Hum Mutat 33:703–709, 2012. © 2012 Wiley Periodicals, Inc

Marker genes in the MCF-7 system.

<p>In each state of a six-state model, genes are ranked by their state-expression fold change with respect to the first state. Here, only the top 50 are shown along with their ranking in the other states. For the top genes of state 2 also the rank assigned considering a maximum fold change criterion over the time course is shown for comparison (separated column). The state-based ranking criterion highlights marker genes which would otherwise pass unnoticed.</p

Fits to gene expression time-course data.

<p>The fit to some key genes, comprising the 11 primary transcription factors identified by Cicatiello et<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Cicatiello1" target="_blank">[4]</a> and other important estrogen-responsive genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Zhu1" target="_blank">[1]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Weisz1" target="_blank">[2]</a>, are shown: black circles represent time-course (standardized) data while green lines represents the gene expression predicted by the six-state model.</p

The number of single-cell states in the MCF-7 response to estrogen.

<p>(<b>A</b>) The mean squared error of the model fit to the microarray data decreases as function of the number of states: as expected, when the number of parameters increases, the quality of the fit improves. (<b>B</b>) The condition number is a measure of the similarity of the transcriptional profiles of the states. It increases as function of the number of states, , highlighting that over-fitting also increases with . A good balance between fit quality and over-fitting must be found. (<b>C</b>) The model posterior probability, derived by a Bayesian approach, has a peak at , which shows that a model with six states strikes a good balance between fit-to-data and model parsimony.</p

Estrogen responding genes per state.

<p>Among the entire gene set considered in the MCF-7 cell experiment, 1270 also responded in ZR-75.1 cells. These are referred to as common ‘estrogen-regulated genes’ (E2R genes) in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Cicatiello1" target="_blank">[4]</a>. ‘Primary genes’ are their subgroup having a ER transcription factor binding site within 10 kb around the TSS. The figures show how E2R and primary genes are responding across the single-cell states of a six-state model. (<b>A</b>) Fraction of up-regulated and down-regulated E2R genes. (<b>B</b>) Fraction of first-responding E2R genes, i.e., of genes that respond for the first time in a given state. (<b>C</b>) and (<b>D</b>) show the analogous pattern of primary genes.</p