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ABSTRACT: CTCF is a highly conserved transcriptional regulator protein that performs diverse functions such as regulating gene expression and organizing the 3D structure of the genome. Here, we describe recent updates to a database of CTCF-binding sites, CTCFBSDB (http://insulatordb.uthsc.edu/), which now contains almost 15 million CTCF-binding sequences in 10 species. Since the original publication of the database, studies of the 3D structure of the genome, such as those provided by Hi-C experiments, have suggested that CTCF plays an important role in mediating intra- and inter-chromosomal interactions. To reflect this important progress, we have integrated CTCF-binding sites with genomic topological domains defined using Hi-C data. Additionally, the updated database includes new features enabled by new CTCF-binding site data, including binding site occupancy and the ability to visualize overlapping CTCF-binding sites determined in separate experiments.
Nucleic Acids Research 11/2012; · 8.03 Impact Factor
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ABSTRACT: Whole-genome sequencing of cancers has begun to identify thousands of somatic mutations that distinguish the genomes of normal tissues from cancers. While many germline mutations within microRNAs (miRNAs) and their targets have been shown to alter miRNA function in cancers and have been associated with cancer risk, the impact of somatic mutations on miRNA function has received relatively little attention. Here, we have created the SomamiR database (http://compbio.uthsc.edu/SomamiR/) to provide a comprehensive resource that integrates several types of data for use in investigating the impact of somatic and germline mutations on miRNA function in cancer. The database contains somatic mutations that may create or disrupt miRNA target sites and integrates these somatic mutations with germline mutations within the same target sites, genome-wide and candidate gene association studies of cancer and functional annotations that link genes containing mutations with cancer. Additionally, the database contains a collection of germline and somatic mutations in miRNAs and their targets that have been experimentally shown to impact miRNA function and have been associated with cancer.
Nucleic Acids Research 11/2012; · 8.03 Impact Factor
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ABSTRACT: MicroRNAs (miRNAs) are small noncoding RNA that play an important role in posttranscriptional regulation of mRNA. Genetic variations in miRNAs or their target sites have been shown to alter miRNA function and have been associated with risk for several diseases. Previous studies have focused on the most abundant type of genetic variations, single nucleotide polymorphisms (SNPs) that affect miRNA-mRNA interactions. Here, we systematically identified small insertions and deletions (indels) in miRNAs and their target sites, and investigated the effects of indels on miRNA targeting. We studied the distribution of indels in miRNAs and their target sites and found that indels in mature miRNAs, experimentally supported miRNA target sites and PAR-CLIP footprints have significantly lower density compared to flanking regions. We identified over 20 indels in the seed regions of miRNAs, which may disrupt the interactions between these miRNAs and their target genes. We also identified hundreds of indels that alter experimentally supported miRNA target sites. We mapped these genes to human disease pathways to identify indels that affect miRNA targeting in these pathways. We also used the results of genome-wide association studies (GWAS) to identify potential links between miRNA-related indels and diseases.
PLoS ONE 01/2012; 7(9):e46176. · 4.09 Impact Factor
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ABSTRACT: Determining the functional impact of somatic mutations is crucial to understanding tumorigenesis and metastasis. Recent sequences of several cancers have provided comprehensive lists of somatic mutations across entire genomes, enabling investigation of the functional impact of somatic mutations in non-coding regions. Here, we study somatic mutations in 3'UTRs of genes that have been identified in four cancers and computationally predict how they may alter miRNA targeting, potentially resulting in dysregulation of the expression of the genes harboring these mutations. We find that somatic mutations create or disrupt putative miRNA target sites in the 3'UTRs of many genes, including several genes, such as MITF, EPHA3, TAL1, SCG3, and GSDMA, which have been previously associated with cancer. We also integrate the somatic mutations with germline mutations and results of association studies. Specifically, we identify putative miRNA target sites in the 3'UTRs of BMPR1B, KLK3, and SPRY4 that are disrupted by both somatic and germline mutations and, also, are in linkage disequilibrium blocks with high scoring markers from cancer association studies. The somatic mutation in BMPR1B is located in a target site of miR-125b; germline mutations in this target site have previously been both shown to disrupt regulation of BMPR1B by miR-125b and linked with cancer.
PLoS ONE 01/2012; 7(10):e47137. · 4.09 Impact Factor
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ABSTRACT: The polymorphism in microRNA target site (PolymiRTS) database aims to identify single-nucleotide polymorphisms (SNPs) that affect miRNA targeting in human and mouse. These polymorphisms can disrupt the regulation of gene expression by miRNAs and are candidate genetic variants responsible for transcriptional and phenotypic variation. The database is therefore organized to provide links between SNPs in miRNA target sites, cis-acting expression quantitative trait loci (eQTLs), and the results of genome-wide association studies (GWAS) of human diseases. Here, we describe new features that have been integrated in the PolymiRTS database, including: (i) polymiRTSs in genes associated with human diseases and traits in GWAS, (ii) polymorphisms in target sites that have been supported by a variety of experimental methods and (iii) polymorphisms in miRNA seed regions. A large number of newly identified microRNAs and SNPs, recently published mouse phenotypes, and human and mouse eQTLs have also been integrated into the database. The PolymiRTS database is available at http://compbio.uthsc.edu/miRSNP/.
Nucleic Acids Research 11/2011; 40(Database issue):D216-21. · 8.03 Impact Factor
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ABSTRACT: Lattice grand canonical Monte Carlo simulations of heteropolymers adsorbing on heterogeneous surfaces are performed to help understand molecular recognition. The heteropolymers are self-avoiding walks of AB copolymers, which interact with a single heterogeneous surface composed of two different types of surface sites, type 1 and type 2. Interactions of type A beads with type 1 sites and type B beads with type 2 sites are attractive, whereas other interactions are neutral. Two polymer chain types with different statistical sequences simultaneously interact with a surface with a defined statistical pattern, enabling the study of the effects of competition for the limited number of surface sites during adsorption. We find that competition results in a significant increase in selectivity. In some cases, for example, the adsorption of alternating sequences on an alternating surface completely suppresses the adsorption of other competing sequence types. The selection rules, however, remain the same as in the noncompetitive adsorption. Surfaces with extreme patterns (i.e., completely alternating or highly patchy) exhibit the highest selectivity toward the matching sequences (completely alternating or blocky, respectively). On moderately patchy or moderately alternating surfaces, sequences with extreme patterns (highly blocky or completely alternating), not the sequences with matching statistics, have the highest selectivity. Random surfaces have little or no selectivity toward different sequence types.
01/2010;
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ABSTRACT: The success of polyethyleneimine (PEI) as a nonviral-based gene delivery vector has been attributed to its proton buffering capacity. Despite the great interest in PEI for its use in nonviral-based gene delivery, the protonation behavior of PEI in solution is not well understood. Earlier experimental studies have reported inconsistent values of the protonation state of PEI. In this work, we report our investigation of the protonation behavior of a realistic linear PEI (lPEI) with computational approaches. Reported experimental pK(a) values of several diamine compounds are first examined. A screened Coulombic interaction with a distance dependence dielectric is shown to reproduce the shifted pK(a) values of the model diamine compounds. Then atomistic molecular dynamic simulations of lPEI chain with 20 repeating units are performed and the results are used to provide parameters for a coarse-grained polyamine model. The screened Coulombic interaction is then incorporated in the coarse-grained lPEI chain and computational titrations are performed. The obtained computational titration curves of lPEI in solutions were found to be in best agreement with experimental results by Smits et al., but the computational titration curves have too strong of a dependence on salt concentration compared to the experimental results by Smits et al. Disregarding the discrepancy in the salt dependence, our computational titrations reveal that approximately 55% of the lPEI amine groups are protonated under physiological conditions in solution with a nearly alternating arrangement of protonated and nonprotonated amines. Titrations of lPEI in the presence of a polyanion are also performed to determine how the charge state of lPEI could be affected by complexation with DNA in gene therapy preparations. While the presence of the polyanion increases the degree of protonation of the PEI, many of PEI amines remain unprotonated under physiological conditions, providing evidence that PEI complexed with DNA could still have proton buffering capacity. Potential sources of error that have resulted in the inconsistency of previously reported protonation states of PEI were also discussed.
Biomacromolecules 12/2009; 11(1):29-38. · 5.48 Impact Factor
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ABSTRACT: Monte Carlo simulations are performed to study selective adsorption of heteropolymers on heterogeneous surfaces. We focus on how statistical correlation between sequence types and surface patterns affects critical adsorption points (CAP), the point that marks the transition of a polymer chain, in contact with a surface, from preferring a nonadsorbed state in bulk solution to an adsorbed state on the surface. A large difference in the CAP’s of different sequence types over the same surface identifies a window of interaction energies where selectivity is maximized. Our results show that statistical random surfaces (i.e, neighboring surface sites have no statistical correlation) cannot differentiate among different heteropolymer sequences. Conversely, random heteropolymers cannot differentiate different surface types. However, when neighboring surface sites have statistical correlations, selective adsorption of different heteropolymers is observed.
06/2008;
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ABSTRACT: The critical adsorption point (CAP) of self-avoiding walks (SAW) interacting with a planar surface with surface disorder or sequence disorder has been studied. We present theoretical equations, based on ones previously developed by Soteros and Whittington (J. Phys. A.: Math. Gen. 2004, 37, R279-R325), that describe the dependence of CAP on the disorders along with Monte Carlo simulation data that are in agreement with the equations. We also show simulation results that deviate from the equations when the approximations used in the theory break down. Such knowledge is the first step toward understanding the correlation of surface disorder and sequence disorder during polymer adsorption. Comment: 29 pages, 8 figures
04/2007;
