Footer: a quantitative comparative genomics method for efficient recognition of cis-regulatory elements

Autotaxin in Pathophysiology and Pulmonary Fibrosis

Lysophospholipid signaling is emerging as a druggable regulator of pathophysiological responses, and especially fibrosis, exemplified by the relative ongoing clinical trials in idiopathic pulmonary fibrosis (IPF) patients. In this review, we focus on ectonucleotide pyrophosphatase-phosphodiesterase 2 (ENPP2), or as more widely known Autotaxin (ATX), a secreted lysophospholipase D (lysoPLD) largely responsible for extracellular lysophosphatidic acid (LPA) production. In turn, LPA is a bioactive phospholipid autacoid, forming locally upon increased ATX levels and acting also locally through its receptors, likely guided by ATX's structural conformation and cell surface associations. Increased ATX activity levels have been detected in many inflammatory and fibroproliferative conditions, while genetic and pharmacologic studies have confirmed a pleiotropic participation of ATX/LPA in different processes and disorders. In pulmonary fibrosis, ATX levels rise in the broncheoalveolar fluid (BALF) and stimulate LPA production. LPA engagement of its receptors activate multiple G-protein mediated signal transduction pathways leading to different responses from pulmonary cells including the production of pro-inflammatory signals from stressed epithelial cells, the modulation of endothelial physiology, the activation of TGF signaling and the stimulation of fibroblast accumulation. Genetic or pharmacologic targeting of the ATX/LPA axis attenuated disease development in animal models, thus providing the proof of principle for therapeutic interventions.

Transcription factors YY1, Sp1 and Sp3 modulate dystrophin Dp71 gene expression in hepatic cells

Dystrophin Dp71, the smallest product encoded by the Duchenne muscular dystrophy gene, is ubiquitously expressed in all non-muscle cells. Although Dp71 is involved in various cellular processes, the mechanisms underlying its expression have been little studied. In hepatic cells, Dp71 expression is down-regulated by the xenobiotic β-naphthoflavone. However, the effectors of this regulation remain unknown. In the present study we aimed at identifying DNA elements and transcription factors involved in Dp71 expression in hepatic cells. Relevant DNA elements on the Dp71 promoter were identified by comparing Dp71 5'-end flanking regions between species. The functionality of these elements was demonstrated by site-directed mutagenesis. Using EMSAs and ChIP, we showed that the Sp1 (specificity protein 1), Sp3 (specificity protein 3) and YY1 (Yin and Yang 1) transcription factors bind to the Dp71 promoter region. Knockdown of Sp1, Sp3 and YY1 in hepatic cells increased endogenous Dp71 expression, but reduced Dp71 promoter activity. In summary, Dp71 expression in hepatic cells is carried out, in part, by YY1-, Sp1- and Sp3-mediated transcription from the Dp71 promoter.

Lipoxins attenuate renal fibrosis by inducing let-7c and suppressing TGFβR1

Lipoxins, which are endogenously produced lipid mediators, promote the resolution of inflammation, and may inhibit fibrosis, suggesting a possible role in modulating renal disease. Here, lipoxin A4 (LXA4) attenuated TGF-β1-induced expression of fibronectin, N-cadherin, thrombospondin, and the notch ligand jagged-1 in cultured human proximal tubular epithelial (HK-2) cells through a mechanism involving upregulation of the microRNA let-7c. Conversely, TGF-β1 suppressed expression of let-7c. In cells pretreated with LXA4, upregulation of let-7c persisted despite subsequent stimulation with TGF-β1. In the unilateral ureteral obstruction model of renal fibrosis, let-7c upregulation was induced by administering an LXA4 analog. Bioinformatic analysis suggested that targets of let-7c include several members of the TGF-β1 signaling pathway, including the TGF-β receptor type 1. Consistent with this, LXA4-induced upregulation of let-7c inhibited both the expression of TGF-β receptor type 1 and the response to TGF-β1. Overexpression of let-7c mimicked the antifibrotic effects of LXA4 in renal epithelia; conversely, anti-miR directed against let-7c attenuated the effects of LXA4. Finally, we observed that several let-7c target genes were upregulated in fibrotic human renal biopsies compared with controls. In conclusion, these results suggest that LXA4-mediated upregulation of let-7c suppresses TGF-β1-induced fibrosis and that expression of let-7c targets is dysregulated in human renal fibrosis.

Towards biological characters of interactions between transcription factors and their DNA targets in mammals

Background

In post-genomic era, the study of transcriptional regulation is pivotal to decode genetic information. Transcription factors (TFs) are central proteins for transcriptional regulation, and interactions between TFs and their DNA targets (TFBSs) are important for downstream genes’ expression. However, the lack of knowledge about interactions between TFs and TFBSs is still baffling people to investigate the mechanism of transcription.

Results

To expand the knowledge about interactions between TFs and TFBSs, three biological features (sequence feature, structure feature, and evolution feature) were utilized to build TFBS identification models for studying binding preference between TFs and their DNA targets in mammals. Results show that each feature does have fairly well performance to capture TFBSs, and the hybrid model combined all three features is more robust for TFBS identification. Subsequently, correspondence between TFs and their TFBSs was investigated to explore interactions among them in mammals. Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.

Conclusions

Our work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions. In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.

p100 Deficiency is insufficient for full activation of the alternative NF-κB pathway: TNF cooperates with p52-RelB in target gene transcription

Background

Constitutive activation of the alternative NF-κB pathway leads to marginal zone B cell expansion and disorganized spleen microarchitecture. Furthermore, uncontrolled alternative NF-κB signaling may result in the development and progression of cancer. Here, we focused on the question how does the constitutive alternative NF-κB signaling exert its effects in these malignant processes.

Methodology/Principal Findings

To explore the consequences of unrestricted alternative NF-κB activation on genome-wide transcription, we compared gene expression profiles of wild-type and NF-κB2/p100-deficient (p100^−/−) primary mouse embryonic fibroblasts (MEFs) and spleens. Microarray experiments revealed only 73 differentially regulated genes in p100^−/− vs. wild-type MEFs. Chromatin immunoprecipitation (ChIP) assays showed in p100^−/− MEFs direct binding of p52 and RelB to the promoter of the Enpp2 gene encoding ENPP2/Autotaxin, a protein with an important role in lymphocyte homing and cell migration. Gene ontology analysis revealed upregulation of genes with anti-apoptotic/proliferative activity (Enpp2/Atx, Serpina3g, Traf1, Rrad), chemotactic/locomotory activity (Enpp2/Atx, Ccl8), and lymphocyte homing activity (Enpp2/Atx, Cd34). Most importantly, biochemical and gene expression analyses of MEFs and spleen, respectively, indicated a marked crosstalk between classical and alternative NF-κB pathways.

Conclusions/Significance

Our results show that p100 deficiency alone was insufficient for full induction of genes regulated by the alternative NF-κB pathway. Moreover, alternative NF-κB signaling strongly synergized both in vitro and in vivo with classical NF-κB activation, thereby extending the number of genes under the control of the p100 inhibitor of the alternative NF-κB signaling pathway.

CLARE: Cracking the LAnguage of Regulatory Elements

CLARE is a computational method designed to reveal sequence encryption of tissue-specific regulatory elements. Starting with a set of regulatory elements known to be active in a particular tissue/process, it learns the sequence code of the input set and builds a predictive model from features specific to those elements. The resulting model can then be applied to user-supplied genomic regions to identify novel candidate regulatory elements. CLARE's model also provides a detailed analysis of transcription factors that most likely bind to the elements, making it an invaluable tool for understanding mechanisms of tissue-specific gene regulation.

AVAILABILITY

CLARE is freely accessible at http://clare.dcode.org/.

An alignment-free method to identify candidate orthologous enhancers in multiple Drosophila genomes

MOTIVATION

Evolutionarily conserved non-coding genomic sequences represent a potentially rich source for the discovery of gene regulatory region such as transcriptional enhancers. However, detecting orthologous enhancers using alignment-based methods in higher eukaryotic genomes is particularly challenging, as regulatory regions can undergo considerable sequence changes while maintaining their functionality.

RESULTS

We have developed an alignment-free method which identifies conserved enhancers in multiple diverged species. Our method is based on similarity metrics between two sequences based on the co-occurrence of sequence patterns regardless of their order and orientation, thus tolerating sequence changes observed in non-coding evolution. We show that our method is highly successful in detecting orthologous enhancers in distantly related species without requiring additional information such as knowledge about transcription factors involved, or predicted binding sites. By estimating the significance of similarity scores, we are able to discriminate experimentally validated functional enhancers from seemingly equally conserved candidates without function. We demonstrate the effectiveness of this approach on a wide range of enhancers in Drosophila, and also present encouraging results to detect conserved functional regions across large evolutionary distances. Our work provides encouraging steps on the way to ab initio unbiased enhancer prediction to complement ongoing experimental efforts.

AVAILABILITY

The software, data and the results used in this article are available at http://www.genome.duke.edu/labs/ohler/research/transcription/fly_enhancer/.

Inhibition and role of let-7d in idiopathic pulmonary fibrosis

RATIONALE

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and usually lethal fibrotic lung disease characterized by profound changes in epithelial cell phenotype and fibroblast proliferation.

OBJECTIVES

To determine changes in expression and role of microRNAs in IPF.

METHODS

RNA from 10 control and 10 IPF tissues was hybridized on Agilent microRNA microarrays and results were confirmed by quantitative real-time polymerase chain reaction and in situ hybridization. SMAD3 binding to the let-7d promoter was confirmed by chromatin immunoprecipitation, electrophoretic mobility shift assay, luciferase assays, and reduced expression of let-7d in response to transforming growth factor-beta. HMGA2, a let-7d target, was localized by immunohistochemistry. In mice, let-7d was inhibited by intratracheal administration of a let-7d antagomir and its effects were determined by immunohistochemistry, immunofluorescence, quantitative real-time polymerase chain reaction, and morphometry.

MEASUREMENTS AND MAIN RESULTS

Eighteen microRNAs including let-7d were significantly decreased in IPF. Transforming growth factor-beta down-regulated let-7d expression, and SMAD3 binding to the let-7d promoter was demonstrated. Inhibition of let-7d caused increases in mesenchymal markers N-cadherin-2, vimentin, and alpha-smooth muscle actin (ACTA2) as well as HMGA2 in multiple epithelial cell lines. let-7d was significantly reduced in IPF lungs and the number of epithelial cells expressing let-7d correlated with pulmonary functions. HMGA2 was increased in alveolar epithelial cells of IPF lungs. let-7d inhibition in vivo caused alveolar septal thickening and increases in collagen, ACTA2, and S100A4 expression in SFTPC (pulmonary-associated surfactant protein C) expressing alveolar epithelial cells.

CONCLUSIONS

Our results indicate a role for microRNAs in IPF. The down-regulation of let-7d in IPF and the profibrotic effects of this down-regulation in vitro and in vivo suggest a key regulatory role for this microRNA in preventing lung fibrosis. Clinical trial registered with www.clinicaltrials.gov (NCT 00258544).

Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data

Background

MicroRNAs (miRNAs) are short, non-coding RNA regulators of protein coding genes. miRNAs play a very important role in diverse biological processes and various diseases. Many algorithms are able to predict miRNA genes and their targets, but their transcription regulation is still under investigation. It is generally believed that intragenic miRNAs (located in introns or exons of protein coding genes) are co-transcribed with their host genes and most intergenic miRNAs transcribed from their own RNA polymerase II (Pol II) promoter. However, the length of the primary transcripts and promoter organization is currently unknown.

Methodology

We performed Pol II chromatin immunoprecipitation (ChIP)-chip using a custom array surrounding regions of known miRNA genes. To identify the true core transcription start sites of the miRNA genes we developed a new tool (CPPP). We showed that miRNA genes can be transcribed from promoters located several kilobases away and that their promoters share the same general features as those of protein coding genes. Finally, we found evidence that as many as 26% of the intragenic miRNAs may be transcribed from their own unique promoters.

Conclusion

miRNA promoters have similar features to those of protein coding genes, but miRNA transcript organization is more complex.

Regulatory conservation of protein coding and microRNA genes in vertebrates: lessons from the opossum genome

A study of conservation of non-coding sequences, cis-regulatory elements and biological functions of regulated genes in opossum and other vertebrates enables better estimation of promoter conservation and transcription factor binding site turnover among mammals

Background

Being the first noneutherian mammal sequenced, Monodelphis domestica (opossum) offers great potential for enhancing our understanding of the evolutionary processes that take place in mammals. This study focuses on the evolutionary relationships between conservation of noncoding sequences, cis-regulatory elements, and biologic functions of regulated genes in opossum and eight vertebrate species.

Results

Analysis of 145 intergenic microRNA and all protein coding genes revealed that the upstream sequences of the former are up to twice as conserved as the latter among mammals, except in the first 500 base pairs, where the conservation is similar. Comparison of promoter conservation in 513 protein coding genes and related transcription factor binding sites (TFBSs) showed that 41% of the known human TFBSs are located in the 6.7% of promoter regions that are conserved between human and opossum. Some core biologic processes exhibited significantly fewer conserved TFBSs in human-opossum comparisons, suggesting greater functional divergence. A new measure of efficiency in multigenome phylogenetic footprinting (base regulatory potential rate [BRPR]) shows that including human-opossum conservation increases specificity in finding human TFBSs.

Conclusion

Opossum facilitates better estimation of promoter conservation and TFBS turnover among mammals. The fact that substantial TFBS numbers are located in a small proportion of the human-opossum conserved sequences emphasizes the importance of marsupial genomes for phylogenetic footprinting-based motif discovery strategies. The BRPR measure is expected to help select genome combinations for optimal performance of these algorithms. Finally, although the etiology of the microRNA upstream increased conservation remains unknown, it is expected to have strong implications for our understanding of regulation of their expression.

New insights into mathematical modeling of the immune system

In order to understand the integrated behavior of the immune system, there is no alternative to mathematical modeling. In addition, the advent of experimental tools such as gene arrays and proteomics poses new challenges to immunologists who are now faced with more information than can be readily incorporated into existing paradigms of immunity. We review here our ongoing efforts to develop mathematical models of immune responses to infectious disease, highlight a new modeling approach that is more accessible to immunologists, and describe new ways to analyze microarray data. These are collaborative studies between experimental immunologists, mathematicians, and computer scientists.

DNA familial binding profiles made easy: comparison of various motif alignment and clustering strategies

Transcription factor (TF) proteins recognize a small number of DNA sequences with high specificity and control the expression of neighbouring genes. The evolution of TF binding preference has been the subject of a number of recent studies, in which generalized binding profiles have been introduced and used to improve the prediction of new target sites. Generalized profiles are generated by aligning and merging the individual profiles of related TFs. However, the distance metrics and alignment algorithms used to compare the binding profiles have not yet been fully explored or optimized. As a result, binding profiles depend on TF structural information and sometimes may ignore important distinctions between subfamilies. Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP–chip data where frequently multiple putative targets of usually unknown TFs are predicted. Various comparison metrics and alignment algorithms are evaluated (a total of 105 combinations). We find that local alignments are generally better than global alignments at detecting eukaryotic DNA motif similarities, especially when combined with the sum of squared distances or Pearson's correlation coefficient comparison metrics. In addition, multiple-alignment strategies for binding profiles and tree-building methods are tested for their efficiency in constructing generalized binding models. A new method for automatic determination of the optimal number of clusters is developed and applied in the construction of a new set of familial binding profiles which improves upon TF classification accuracy. A software tool, STAMP, is developed to host all tested methods and make them publicly available. This work provides a high quality reference set of familial binding profiles and the first comprehensive platform for analysis of DNA profiles. Detecting similarities between DNA motifs is a key step in the comparative study of transcriptional regulation, and the work presented here will form the basis for tool and method development for future transcriptional modeling studies.

Transcription factors are primary regulators of gene expression. They usually recognize short DNA sequences in gene promoters and subsequently alter their transcription rate. It is known that structurally related transcription factors often recognize similar DNA-binding patterns (or motifs). Comparison of these motifs not only provides insights into the evolutionary process they undergo, but it also has many important practical applications. For example, motifs that are found to be “similar” can be combined to form generalized profiles, which can be used to improve our ability to predict novel DNA signals in the promoters of co-expressed genes, and thus facilitate a more accurate mapping of gene-regulatory networks. However, to date there is no comprehensive platform that will allow for an efficient analysis of DNA motifs. Furthermore, the efficiency of the methods used to assign similarity between DNA motifs has not been thoroughly tested. This paper takes an important first step towards this goal by evaluating available comparison strategies as applied to DNA motifs and by generating an improved familial profile dataset.

WeederH: an algorithm for finding conserved regulatory motifs and regions in homologous sequences

Background

This work addresses the problem of detecting conserved transcription factor binding sites and in general regulatory regions through the analysis of sequences from homologous genes, an approach that is becoming more and more widely used given the ever increasing amount of genomic data available.

Results

We present an algorithm that identifies conserved transcription factor binding sites in a given sequence by comparing it to one or more homologs, adapting a framework we previously introduced for the discovery of sites in sequences from co-regulated genes. Differently from the most commonly used methods, the approach we present does not need or compute an alignment of the sequences investigated, nor resorts to descriptors of the binding specificity of known transcription factors. The main novel idea we introduce is a relative measure of conservation, assuming that true functional elements should present a higher level of conservation with respect to the rest of the sequence surrounding them. We present tests where we applied the algorithm to the identification of conserved annotated sites in homologous promoters, as well as in distal regions like enhancers.

Conclusion

Results of the tests show how the algorithm can provide fast and reliable predictions of conserved transcription factor binding sites regulating the transcription of a gene, with better performances than other available methods for the same task. We also show examples on how the algorithm can be successfully employed when promoter annotations of the genes investigated are missing, or when regulatory sites and regions are located far away from the genes.

Web-based identification of evolutionary conserved DNA cis-regulatory elements

Transcription regulation on a gene-by-gene basis is achieved through transcription factors, the DNA-binding proteins that recognize short DNA sequences in the proximity of the genes. Unlike other DNA-binding proteins, each transcription factor recognizes a number of sequences, usually variants of a preferred, "consensus" sequence. The degree of dissimilarity of a given target sequence from the consensus is indicative of the binding affinity of the transcription factor-DNA interaction. Because of the short size and the degeneracy of the patterns, it is frequently difficult for a computational algorithm to distinguish between the true sites and the background genomic "noise." One way to overcome this problem of low signal-to-noise ratio is to use evolutionary information to detect signals that are conserved in two or more species. FOOTER is an algorithm that uses this phylogenetic footprinting concept and evaluates putative mammalian transcription factor binding sites in a quantitative way. The user is asked to upload the human and mouse promoter sequences and select the transcription factors to be analyzed. The results' page presents an alignment of the two sequences (color-coded by degree of conservation) and information about the predicted sites and single-nucleotide polymorphisms found around the predicted sites. This chapter presents the main aspects of the underlying method and gives detailed instructions and tips on the use of this web-based tool.

Computational identification of transcriptional regulatory elements in DNA sequence

Identification and annotation of all the functional elements in the genome, including genes and the regulatory sequences, is a fundamental challenge in genomics and computational biology. Since regulatory elements are frequently short and variable, their identification and discovery using computational algorithms is difficult. However, significant advances have been made in the computational methods for modeling and detection of DNA regulatory elements. The availability of complete genome sequence from multiple organisms, as well as mRNA profiling and high-throughput experimental methods for mapping protein-binding sites in DNA, have contributed to the development of methods that utilize these auxiliary data to inform the detection of transcriptional regulatory elements. Progress is also being made in the identification of cis-regulatory modules and higher order structures of the regulatory sequences, which is essential to the understanding of transcription regulation in the metazoan genomes. This article reviews the computational approaches for modeling and identification of genomic regulatory elements, with an emphasis on the recent developments, and current challenges.

FOOTER: a web tool for finding mammalian DNA regulatory regions using phylogenetic footprinting

FOOTER is a newly developed algorithm that analyzes homologous mammalian promoter sequences in order to identify transcriptional DNA regulatory 'signals'. FOOTER uses prior knowledge about the binding site preferences of the transcription factors (TFs) in the form of position-specific scoring matrices (PSSMs). The PSSM models are generated from known mammalian binding sites from the TRANSFAC database. In a test set of 72 confirmed binding sites (most of them not present in TRANSFAC) of 19 TFs, it exhibited 83% sensitivity and 72% specificity. FOOTER is accessible over the web at http://biodev.hgen.pitt.edu/Footer/.