Genome-wide Transcription Factor Binding Research Articles

Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells

BackgroundRecent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes.ResultsWe show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epigenetic modifications (histone modifications and promoter CpG methylation status), which are known to play a role in controlling gene expression during ES cell self-renewal and differentiation.ConclusionOur systems biologic re-analysis of gene expression, transcription factor binding, epigenetic and gene ontology data provides a novel integrative view of ES cell biology.

GPAT: Retrieval of genomic annotation from large genomic position datasets

BackgroundRecent genome wide transcription factor binding site or chromatin modification mapping analysis techniques, such as chromatin immunoprecipitation (ChIP) linked to DNA microarray analysis (ChIP on chip) or ChIP coupled to high throughput sequencing (ChIP-seq), generate tremendous amounts of genomic location data in the form of one-dimensional series of signals. After pre-analysis of these data (signal pre-clearing, relevant binding site detection), biologists need to search for the biological relevance of the detected genomic positions representing transcription regulation or chromatin modification events.ResultsTo address this problem, we have developed a Genomic Position Annotation Tool (GPAT) with a simple web interface that allows the rapid and systematic labelling of thousands of genomic positions with several types of annotations. GPAT automatically extracts gene annotation information around the submitted positions from different public databases (Refseq or ENSEMBL). In addition, GPAT provides access to the expression status of the corresponding genes from either existing transcriptomic databases or from user generated expression data sets. Furthermore, GPAT allows the localisation of the genomic coordinates relative to the chromosome bands and the well characterised ENCODE regions. We successfully used GPAT to analyse ChIP on chip data and to identify genes functionally regulated by the TATA binding protein (TBP).ConclusionGPAT provides a quick, convenient and flexible way to annotate large sets of genomic positions obtained after pre-analysis of ChIP-chip, ChIP-seq or other high throughput sequencing-based techniques. Through the different annotation data displayed, GPAT facilitates the interpretation of genome wide datasets for molecular biologists.

Prediction and Testing of Novel Transcriptional Networks Regulating Embryonic Stem Cell Self-Renewal and Commitment

Stem cell fate is governed by the integration of intrinsic and extrinsic positive and negative signals upon inherent transcriptional networks. To identify novel embryonic stem cell (ESC) regulators and assemble transcriptional networks controlling ESC fate, we performed temporal expression microarray analyses of ESCs after the initiation of commitment and integrated these data with known genome-wide transcription factor binding. Effects of forced under- or overexpression of predicted novel regulators, defined as differentially expressed genes with potential binding sites for known regulators of pluripotency, demonstrated greater than 90% correspondence with predicted function, as assessed by functional and high-content assays of self-renewal. We next assembled 43 theoretical transcriptional networks in ESCs, 82% (23 out of 28 tested) of which were supported by analysis of genome-wide expression in Oct4 knockdown cells. By using this integrative approach, we have formulated novel networks describing gene repression of key developmental regulators in undifferentiated ESCs and successfully predicted the outcomes of genetic manipulation of these networks.

Quantifying DNA–protein binding specificities by using oligonucleotide mass tags and mass spectroscopy

The ability to determine the relative binding affinity of different transcription-factors (TF) to their DNA binding sites is fundamentally important for a comprehensive understanding of gene regulation. Here we present a general approach for multiplex quantification of DNA-TF binding specificities in vitro using oligonucleotide mass tag (OMT) labeling and mass spectroscopic quantification. An OMT is a short nucleic acid sequence with a distinct mass that can be resolved by a mass spectrometer. Each putative binding sequence is labeled with a unique OMT, and PCR amplification of OMTs is performed after removing nonbound DNA. Subsequently, a primer extension reaction is carried out, and the extension products are quantified by MALDI-TOF mass spectroscopy. Using the TF NF-kappaB P50, we have quantified the binding specificities of up to 15 binding sequences in a single assay. The results from the multiplex assay are consistent with data from the traditional gel shift assay. The approach allows the competitive binding of multiple DNA sequences to the given protein in a homogeneous reaction. By using the commercially available homogeneous MassEXTEND platform (SEQUENOM), it is scalable for high-throughput DNA-TF binding applications, including genome-wide TF binding site mapping and analyses of SNPs in promoter regions.

Modelling the network of cell cycle transcription factors in the yeast Saccharomyces cerevisiae.

BackgroundReverse-engineering regulatory networks is one of the central challenges for computational biology. Many techniques have been developed to accomplish this by utilizing transcription factor binding data in conjunction with expression data. Of these approaches, several have focused on the reconstruction of the cell cycle regulatory network of Saccharomyces cerevisiae. The emphasis of these studies has been to model the relationships between transcription factors and their target genes. In contrast, here we focus on reverse-engineering the network of relationships among transcription factors that regulate the cell cycle in S. cerevisiae.ResultsWe have developed a technique to reverse-engineer networks of the time-dependent activities of transcription factors that regulate the cell cycle in S. cerevisiae. The model utilizes linear regression to first estimate the activities of transcription factors from expression time series and genome-wide transcription factor binding data. We then use least squares to construct a model of the time evolution of the activities. We validate our approach in two ways: by demonstrating that it accurately models expression data and by demonstrating that our reconstructed model is similar to previously-published models of transcriptional regulation of the cell cycle.ConclusionOur regression-based approach allows us to build a general model of transcriptional regulation of the yeast cell cycle that includes additional factors and couplings not reported in previously-published models. Our model could serve as a starting point for targeted experiments that test the predicted interactions. In the future, we plan to apply our technique to reverse-engineer other systems where both genome-wide time series expression data and transcription factor binding data are available.

An initial blueprint for myogenic differentiation

We have combined genome-wide transcription factor binding and expression profiling to assemble a regulatory network controlling the myogenic differentiation program in mammalian cells. We identified a cadre of overlapping and distinct targets of the key myogenic regulatory factors (MRFs)--MyoD and myogenin--and Myocyte Enhancer Factor 2 (MEF2). We discovered that MRFs and MEF2 regulate a remarkably extensive array of transcription factor genes that propagate and amplify the signals initiated by MRFs. We found that MRFs play an unexpectedly wide-ranging role in directing the assembly and usage of the neuromuscular junction. Interestingly, these factors also prepare myoblasts to respond to diverse types of stress. Computational analyses identified novel combinations of factors that, depending on the differentiation state, might collaborate with MRFs. Our studies suggest unanticipated biological insights into muscle development and highlight new directions for further studies of genes involved in muscle repair and responses to stress and damage.

Negative information for motif discovery.

We discuss a method of combining genome-wide transcription factor binding data, gene expression data, and genome sequence data for the purpose of motif discovery in S. cerevisiae. Within the word-counting algorithmic approach to motif discovery, we present a method of incorporating information from negative intergenic regions where a transcription factor is thought not to bind, and a statistical significance measure which account for intergenic regions of different lengths. Our results demonstrate that our method performs slightly better than other motif discovery algorithms. Finally, we present significant potential new motifs discovered by the algorithm.