Transcription Factor Dynamics Research Articles

MirConnX: condition-specific mRNA-microRNA network integrator

mirConnX is a user-friendly web interface for inferring, displaying and parsing mRNA and microRNA (miRNA) gene regulatory networks. mirConnX combines sequence information with gene expression data analysis to create a disease-specific, genome-wide regulatory network. A prior, static network has been constructed for all human and mouse genes. It consists of computationally predicted transcription factor (TF)-gene associations and miRNA target predictions. The prior network is supplemented with known interactions from the literature. Dynamic TF- and miRNA-gene associations are inferred from user-provided expression data using an association measure of choice. The static and dynamic networks are then combined using an integration function with user-specified weights. Visualization of the network and subsequent analysis are provided via a very responsive graphic user interface. Two organisms are currently supported: Homo sapiens and Mus musculus. The intuitive user interface and large database make mirConnX a useful tool for clinical scientists for hypothesis generation and explorations. mirConnX is freely available for academic use at http://www.benoslab.pitt.edu/mirconnx.

Dynamic combinatorial interactions of RUNX1 and cooperating partners regulates megakaryocytic differentiation in cell line models

AbstractSpecific interactions of transcription factors (TFs) with their targets are crucial for specifying gene expression programs during cell differentiation. How specificity is maintained despite limited selectivity of individual TF-DNA interactions is not fully understood. RUNX1 TF is among the most frequently mutated genes in human leukemia and an important regulator of megakaryopoiesis. We used megakaryocytic cell lines to characterize the network of RUNX1 targets and cooperating TFs in differentiating megakaryocytes and demonstrated how dynamic partnerships between RUNX1 and cooperating TFs facilitated regulatory plasticity and specificity during this process. After differentiation onset, RUNX1 directly activated a large number of genes through interaction with preexisting and de novo binding sites. Recruitment of RUNX1 to de novo occupied sites occurred at H3K4me1-marked preprogrammed enhancers. A significant number of these de novo bound sites lacked RUNX motif but were occupied by AP-1 TFs. Reciprocally, AP-1 TFs were up-regulated by RUNX1 after 12-O-tetradecanoylphorbol-13-acetate induction and recruited to RUNX1-occupied sites lacking AP-1 motifs. At other differentiation stages, additional combinatorial interactions occurred between RUNX1 and its coregulators, GATA1 and ETS. The findings suggest that in differentiating megakaryocytic cell lines, RUNX1 cooperates with GATA1, AP-1, and ETS to orchestrate cell-specific transcription programs through dynamic TF partnerships.

Learning combinatorial transcriptional dynamics from gene expression data

mRNA transcriptional dynamics is governed by a complex network of transcription factor (TF) proteins. Experimental and theoretical analysis of this process is hindered by the fact that measurements of TF activity in vivo is very challenging. Current models that jointly infer TF activities and model parameters rely on either of the two main simplifying assumptions: either the dynamics is simplified (e.g. assuming quasi-steady state) or the interactions between TFs are ignored, resulting in models accounting for a single TF. We present a novel approach to reverse engineer the dynamics of multiple TFs jointly regulating the expression of a set of genes. The model relies on a continuous time, differential equation description of transcriptional dynamics where TFs are treated as latent on/off variables and are modelled using a switching stochastic process (telegraph process). The model can not only incorporate both activation and repression, but allows any non-trivial interaction between TFs, including AND and OR gates. By using a factorization assumption within a variational Bayesian treatment we formulate a framework that can reconstruct both the activity profiles of the TFs and the type of regulation from time series gene expression data. We demonstrate the identifiability of the model on a simple but non-trivial synthetic example, and then use it to formulate non-trivial predictions about transcriptional control during yeast metabolism. http://homepages.inf.ed.ac.uk/gsanguin/ g.sanguinetti@ed.ac.uk Supplementary data are available at Bioinformatics online.

Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture

Although several studies have provided important insights into the general principles of biological networks, the link between network organization and the genome-scale dynamics of the underlying entities (genes, mRNAs, and proteins) and its role in systems behavior remain unclear. Here we show that transcription factor (TF) dynamics and regulatory network organization are tightly linked. By classifying TFs in the yeast regulatory network into three hierarchical layers (top, core, and bottom) and integrating diverse genome-scale datasets, we find that the TFs have static and dynamic properties that are similar within a layer and different across layers. At the protein level, the top-layer TFs are relatively abundant, long-lived, and noisy compared with the core- and bottom-layer TFs. Although variability in expression of top-layer TFs might confer a selective advantage, as this permits at least some members in a clonal cell population to initiate a response to changing conditions, tight regulation of the core- and bottom-layer TFs may minimize noise propagation and ensure fidelity in regulation. We propose that the interplay between network organization and TF dynamics could permit differential utilization of the same underlying network by distinct members of a clonal cell population.

Analysis of transcription factor expression during oogenesis and preimplantation development in mice

The transition from a differentiated germ cell into a totipotent zygote during oogenesis and preimplantation development is critical to the creation of a new organism. During this period, cell characteristics change dynamically, suggesting that a global alteration of gene expression patterns occurs, which is regulated by global changes in various epigenetic factors. Among these, transcription factors (TFs) are essential in the direct regulation of transcription and also play important roles in determining cell characteristics. However, no comprehensive analysis of TFs from germ cells to embryos had been undertaken. We used mRNA amplification systems and microarrays to conduct a genomewide analysis of TFs at various stages of oogenesis and preimplantation development. The greatest alteration in TFs occurred between the 1- and 2-cell stages, at which time zygotic genome activation (ZGA) occurs. Our analysis of TFs classified by structure and function revealed several specific patterns of change. Basic transcription factors, which are the general components of transcription, increased transiently at the 2-cell stage, while homeodomain (HD) TFs were expressed specifically in the oocyte. TFs containing the Rel homology region (RHR) and Ets domains were expressed at a high level in 2-cell and blastocyst embryos. Thus, the global TF dynamics that occur during oogenesis and preimplantation development seem to regulate the transition from germ-cell-type to embryo-type gene expression.