Computational approaches to finding and analyzing cis-regulatory elements

Discovery and information-theoretic characterization of transcription factor binding sites that act cooperatively

Transcription factor binding to the surface of DNA regulatory regions is one of the primary causes of regulating gene expression levels. A probabilistic approach to model protein-DNA interactions at the sequence level is through position weight matrices (PWMs) that estimate the joint probability of a DNA binding site sequence by assuming positional independence within the DNA sequence. Here we construct conditional PWMs that depend on the motif signatures in the flanking DNA sequence, by conditioning known binding site loci on the presence or absence of additional binding sites in the flanking sequence of each site's locus. Pooling known sites with similar flanking sequence patterns allows for the estimation of the conditional distribution function over the binding site sequences. We apply our model to the Dorsal transcription factor binding sites active in patterning the Dorsal-Ventral axis of Drosophila development. We find that those binding sites that cooperate with nearby Twist sites on average contain about 0.5 bits of information about the presence of Twist transcription factor binding sites in the flanking sequence. We also find that Dorsal binding site detectors conditioned on flanking sequence information make better predictions about what is a Dorsal site relative to background DNA than detection without information about flanking sequence features.

Transgenesis and imaging in birds, and available transgenic reporter lines

Avian embryos are important model organism to study higher vertebrate development. Easy accessibility to developing avian embryos enables a variety of experimental applications to understand specific functions of molecules, tissue-tissue interactions, and cell lineages. The whole-mount ex ovo culture technique for avian embryos permits time-lapse imaging analysis for a better understanding of cell behaviors underlying tissue morphogenesis in physiological conditions. To study mechanisms of blood vessel formation and remodeling in developing embryos by using a time-lapse imaging approach, a transgenic quail model, Tg(tie1:H2B-eYFP), was generated. From a cell behavior perspective, Tg(tie1:H2B-eYFP) quail embryos are a suitable model to shed light on how the structure and pattern of blood vessels are established in higher vertebrates. In this manuscript, we give an overview on the biological and technological background of the transgenic quail model and describe procedures for the ex ovo culture of quail embryos and time-lapse imaging analysis.

Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species

A major goal of bioinformatics is the characterization of transcription factors and the transcriptional programs they regulate. Given the speed of genome sequencing, we would like to quickly annotate regulatory sequences in newly-sequenced genomes. In such cases, it would be helpful to predict sequence motifs by using experimental data from closely related model organism. Here we present a general algorithm that allow to identify transcription factor binding sites in one newly sequenced species by performing Bayesian regression on the annotated species. First we set the rationale of our method by applying it within the same species, then we extend it to use data available in closely related species. Finally, we generalise the method to handle the case when a certain number of experiments, from several species close to the species on which to make inference, are available. In order to show the performance of the method, we analyse three functionally related networks in the Ascomycota. Two gene network case studies are related to the G2/M phase of the Ascomycota cell cycle; the third is related to morphogenesis. We also compared the method with MatrixReduce and discuss other types of validation and tests. The first network is well known and provides a biological validation test of the method. The two cell cycle case studies, where the gene network size is conserved, demonstrate an effective utility in annotating new species sequences using all the available replicas from model species. The third case, where the gene network size varies among species, shows that the combination of information is less powerful but is still informative. Our methodology is quite general and could be extended to integrate other high-throughput data from model organisms.

Inflammatory gene regulatory networks in amnion cells following cytokine stimulation: translational systems approach to modeling human parturition

A majority of the studies examining the molecular regulation of human labor have been conducted using single gene approaches. While the technology to produce multi-dimensional datasets is readily available, the means for facile analysis of such data are limited. The objective of this study was to develop a systems approach to infer regulatory mechanisms governing global gene expression in cytokine-challenged cells in vitro, and to apply these methods to predict gene regulatory networks (GRNs) in intrauterine tissues during term parturition. To this end, microarray analysis was applied to human amnion mesenchymal cells (AMCs) stimulated with interleukin-1β, and differentially expressed transcripts were subjected to hierarchical clustering, temporal expression profiling, and motif enrichment analysis, from which a GRN was constructed. These methods were then applied to fetal membrane specimens collected in the absence or presence of spontaneous term labor. Analysis of cytokine-responsive genes in AMCs revealed a sterile immune response signature, with promoters enriched in response elements for several inflammation-associated transcription factors. In comparison to the fetal membrane dataset, there were 34 genes commonly upregulated, many of which were part of an acute inflammation gene expression signature. Binding motifs for nuclear factor-κB were prominent in the gene interaction and regulatory networks for both datasets; however, we found little evidence to support the utilization of pathogen-associated molecular pattern (PAMP) signaling. The tissue specimens were also enriched for transcripts governed by hypoxia-inducible factor. The approach presented here provides an uncomplicated means to infer global relationships among gene clusters involved in cellular responses to labor-associated signals.

An integrative genomics approach identifies Hypoxia Inducible Factor-1 (HIF-1)-target genes that form the core response to hypoxia

The transcription factor Hypoxia-inducible factor 1 (HIF-1) plays a central role in the transcriptional response to oxygen flux. To gain insight into the molecular pathways regulated by HIF-1, it is essential to identify the downstream-target genes. We report here a strategy to identify HIF-1-target genes based on an integrative genomic approach combining computational strategies and experimental validation. To identify HIF-1-target genes microarrays data sets were used to rank genes based on their differential response to hypoxia. The proximal promoters of these genes were then analyzed for the presence of conserved HIF-1-binding sites. Genes were scored and ranked based on their response to hypoxia and their HIF-binding site score. Using this strategy we recovered 41% of the previously confirmed HIF-1-target genes that responded to hypoxia in the microarrays and provide a catalogue of predicted HIF-1 targets. We present experimental validation for ANKRD37 as a novel HIF-1-target gene. Together these analyses demonstrate the potential to recover novel HIF-1-target genes and the discovery of mammalian-regulatory elements operative in the context of microarray data sets.

Identification of regulatory elements in 16S rRNA gene of Acinetobacter species isolated from water sample

A bacterial strain, designated AcBz01, was isolated from a water sample collected from Gomti River, Lucknow, India, and identified using a molecular approach. On the basis of the bacterial 16S rRNA gene sequence phylogeny and comparison of this gene sequence with sequences in Ribosomal Database project II, evidence given in this study, it is proposed that isolate is closely related to members of the genus Acinetobacter. Identification and annotation of regulatory elements in the 16S rRNA gene and characterization of their interaction with the respective transcription factor can provide basis for better understanding of the mechanism of network of gene interaction of functionally related genes. The identification of such sites is relevant for locating promoter boundary of a gene and it also allows the prediction of specific gene expression pattern and response to disturbances in a known signaling pathway. Computational identification of regulatory elements and Transcription Factor with their binding sites in 16S rRNA gene of Acinetobacter sp. was performed using BPROM tool. We predicted the regulatory elements are TSS, -10 box, -35 box and three Transcription Factor (narP, ompR and fadR) with their binding sites in the upstream region of 16S rRNA gene of Acinetobacter sp. AcBz01. The GenBank accession number for 16S rRNA gene of Acinetobacter sp. AcBz01 is EU931637.