Position Frequency Matrix Research Articles

Regulatory Relationship between the Keap1/Nrf2/ARE Signaling System and Transcriptional Regulators of Lysosomal Biogenesis

Despite the key role of the Keap1/Nrf2/ARE redox-sensitive signaling system in cellular metabolism, little is known about its relationship to lysosome biogenesis. In this paper, a theoretical and experimental analysis of the possibility of such a link has been carried out. By forming a position frequency matrix in the transcription factor genes TFEB and TFE3, the presence of a large number of ARE-like sequences was found in the non-coding regions. In vitro exposure to J774 cells by Keap1/Nrf2/ARE activators (original synthetic monophenol TS-13 and tert-butylhydroquinone as comparison compound) results in dose-dependent induction of Tfe3 and Tfeb genes, accompanied by a gradual increase in the lysosome number and autosomal-lysosomal fusion intensity. Thus, it can be assumed that the proteins controlling the ARE-dependent genes are able to influence lysosome biogenesis.

GSCNN: a composition of CNN and Gibb Sampling computational strategy for predicting promoter in bacterial genomes

This paper is about a new computational strategy to find the promoter sequence which is a transcription starting factor in DNA without which the gene sequence cannot be activated for further protein synthesis process. Genes are the hereditary source of organisms’ functionality of living things. Sequence of activities is carried out to reveal the protein production process hidden in the genes. In this regard promoter prediction is essential to bring out correctness of protein synthesis in decoding the genetical information. Also it enables the biologists to find the genetical malfunctions. Nowadays numerous computational methods which have been proposed are helpful to the biologist in related tasks. Earlier works applied Gibb sampling method, Markov Chain Model and machine learning strategies to predict the promoters. In this paper a new computational strategy named as GSCNN is introduced which combines Gibb sampling with Convolution Neural Network (CNN). This model uses Gibb sampling method to select the best profile features of DNA and extract the scores as feature vector with the help of position frequency matrix. Those extracted feature vectors are further fed into Convolution Neural Network to predict the sigma (σ) 54 promoter of bacterial genome. The performance of the proposed system is evaluated with the help of Bacillus subtilis NC_000964 dataset. The sigma (σ) 54 promoter prediction using the proposed GSCNN method achieved significant improvement in performance metrics compared with the traditional machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbor(KNN), Random Forest(RF) and Support Vector Machine(SVM).

DNA Motif Match Statistics Without Poisson Approximation.

Transcription factors (TFs) play a crucial role in gene regulation by binding to specific regulatory sequences. The sequence motifs recognized by a TF can be described in terms of position frequency matrices. Searching for motif matches with a given position frequency matrix is achieved by employing a predefined score cutoff and subsequently counting the number of matches above this cutoff. In this article, we approximate the distribution of the number of motif matches based on a novel dynamic programming approach, which accounts for higher order sequence background (e.g., as is characteristic for CpG islands) and overlapping motif matches on both DNA strands. A comparison with our previously published compound Poisson approximation and a binomial approximation demonstrates that in particular for relaxed score thresholds, the dynamic programming approach yields more accurate results.

An improved compound Poisson model for the number of motif hits in DNA sequences.

MotivationTranscription factors play a crucial role in gene regulation by binding to specific regulatory sequences. The sequence motifs recognized by a transcription factor can be described in terms of position frequency matrices. When scanning a sequence for matches to a position frequency matrix, one needs to determine a cut-off, which then in turn results in a certain number of hits. In this paper we describe how to compute the distribution of match scores and of the number of motif hits, which are the prerequisites to perform motif hit enrichment analysis.ResultsWe put forward an improved compound Poisson model that supports general order-d Markov background models and which computes the number of motif-hits more accurately than earlier models. We compared the accuracy of the improved compound Poisson model with previously proposed models across a range of parameters and motifs, demonstrating the improvement. The importance of the order-d model is supported in a case study using CpG-island sequences.Availability and implementationThe method is available as a Bioconductor package named ’motifcounter’ https://bioconductor.org/packages/motifcounter.Supplementary information Supplementary data are available at Bioinformatics online.

Abstract A80: NAC1 attenuates BCL6 negative autoregulation and functions as a BCL6 coactivator of FOXQ1 transcription in ovarian cancer (OVCA).

Abstract Nucleus accumbens-associated protein 1 (NAC1) is a transcriptional co-regulator that lacks the DNA binding domain, thus relying on other co-factors to form higher-order transcription complexes. The present study aimed to elucidate the mechanism by which NAC1 modulates gene expression in OVCA. Nacc1-/- mice were euthanized, and spleens were collected. Promoter sequences were retrieved using Biostrings and BSgenome packages of Bioconductor 2.12. The position frequency matrix of BCL6 motif was obtained from Cistrome. BCL6 binding motifs on targeted promoters were identified by matchPWM, and log-odd scores calculated by PWMscoreStartingAt. For chromatin immunoprecipitation (ChIP), cells were fixed with 1% (w/v) paraformaldehyde prior to cross-linking with DTBP. Anti-BCL6, anti-NAC1 or rabbit IgG isotype were used for IP, and the genes of interest were amplified by qPCR. HEK293T cells were used for co-immunoprecipitaion (co-IP), and proteins were analyzed by Western blot. Luciferase reporter assays were performed in HeLa cells transfected with siNAC1 and/or siBCL6. Immunohistochemistry was done on tissue microarray (TMA) constituted of 51 primary ovarian high-grade serous carcinoma samples (HGSOC), using anti-NAC1 or anti-BCL6 antibodies. For microarray experiments, OVCAR5 cells were transfected with siNAC1 or siBCL6. Two-tailed t-test was used analyze data (means ± SD; p<0.05). BCL6 consensus binding motifs were mapped to the promoters of the NAC1 targeted genes FOXQ1 (positions A -1000, B -800, and C -150). NAC1 binds to BCL6 motifs as proved by ChIP and qPCR assays conducted in HeLa and MCF7 cells (p< 0.01). Reciprocal co-IP experiments done in HEK293T cells co-transfected with BCL6-FLAG and full-length NAC1-V5 indicated that NAC1 interacts with BCL6 via its C-terminal domain (NAC1-C186). Functional NAC1 is required to form putative NAC1/BCL6 transcription complex, and to modulate FOXQ1 expression in HeLa N130 tTA cells (p< 0.001). Luciferase reporter assays run in HeLa cells transfected with siNAC1 and siBCL6 proved that the genes interact and collaborate to regulate FOXQ1 transcriptional activity (p< 0.01). NAC1 and BCL6 transcriptional and protein expressions correlate in OVCA cell lines (R2=0.7025; p=0.0185), and in HGSOC TMA (R2=0.31; p=0.027), respectively. Furthermore, NAC1 modulates the expression of BCL6 as shown in OSE-NAC1 transfected cells (p=0.041), and in OVCAR5- (p=0.016), OVCAR3- (p=0.0089), SKOV3- (p=0.0216), and taxol-resistant SKOV3TR-siRNA-NAC1 (p=0.0002) transfected cells. BCL6 transcript expression was reduced by 50% in the spleen of Nacc-/- mice (p=0.0068). Endogenous NAC1 and BCL6 ChIP on the consensus BCL6 binding sequence within the BCL6 promoter in OVCA cells showed that BCL6 binds to its own promoter, and that NAC1 was enriched in the ChIP. Competition assay performed in NAC1-C186-OVCAR-3 cells revealed that BCL6 expression is modulated by NAC1 (p<0.0001), thus pointing to a direct interaction between NAC1 and BCL6 as a key phenomenon that attenuates BCL6 auto-downregulation by NAC1. Modulation of gene expression was assessed by cDNA microarray in siNAC1 or siBCL6 transfected OVCAR5 cells: 238 and 188 genes were down- whereas 139 and 113 genes were up-regulated by NAC1 and BCL6, respectively, amongst which 54 genes were down- and 25 genes were up-regulated concomitantly. In conclusion, our study establishes a new mechanism for NAC1-driven OVCA, in which it interacts with BCL6 via its C-terminal domain forming a transcription complex that modulates the expression of target genes. We have also described a novel function of NAC1 in attenuating BCL6 autoregulation in OVCA. Finally, we have found a significant overlap among NAC1- and BCL6-regulated, suggesting a transcription collaboration of NAC1 and BCL6 in OVCA cells. CORRESPONDING AUTHORS: RANGEL, LBA; SHIH, IM. Citation Format: Alice Laschuk Herlinger, Min Gao, Ren-Chin Wu, Tian-Li Wang, Leticia B A Rangel, Ie-Ming Shih. NAC1 attenuates BCL6 negative autoregulation and functions as a BCL6 coactivator of FOXQ1 transcription in ovarian cancer (OVCA). [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr A80.

JASSA: a comprehensive tool for prediction of SUMOylation sites and SIMs.

Post-translational modification by the Small Ubiquitin-like Modifier (SUMO) proteins, a process termed SUMOylation, is involved in many fundamental cellular processes. SUMO proteins are conjugated to a protein substrate, creating an interface for the recruitment of cofactors harboring SUMO-interacting motifs (SIMs). Mapping both SUMO-conjugation sites and SIMs is required to study the functional consequence of SUMOylation. To define the best candidate sites for experimental validation we designed JASSA, a Joint Analyzer of SUMOylation site and SIMs. JASSA is a predictor that uses a scoring system based on a Position Frequency Matrix derived from the alignment of experimental SUMOylation sites or SIMs. Compared with existing web-tools, JASSA displays on par or better performances. Novel features were implemented towards a better evaluation of the prediction, including identification of database hits matching the query sequence and representation of candidate sites within the secondary structural elements and/or the 3D fold of the protein of interest, retrievable from deposited PDB files. JASSA is freely accessible at http://www.jassa.fr/. Website is implemented in PHP and MySQL, with all major browsers supported. guillaume.beauclair@inserm.fr Supplementary data are available at Bioinformatics online.

An Empirical Prior Improves Accuracy for Bayesian Estimation of Transcription Factor Binding Site Frequencies within Gene Promoters.

A Bayesian method for sampling from the distribution of matches to a precompiled transcription factor binding site (TFBS) sequence pattern (conditioned on an observed nucleotide sequence and the sequence pattern) is described. The method takes a position frequency matrix as input for a set of representative binding sites for a transcription factor and two sets of noncoding, 5′ regulatory sequences for gene sets that are to be compared. An empirical prior on the frequency A (per base pair of gene-vicinal, noncoding DNA) of TFBSs is developed using data from the ENCODE project and incorporated into the method. In addition, a probabilistic model for binding site occurrences conditioned on λ is developed analytically, taking into account the finite-width effects of binding sites. The count of TFBS β (conditioned on the observed sequence) is sampled using Metropolis–Hastings with an information entropy-based move generator. The derivation of the method is presented in a step-by-step fashion, starting from specific conditional independence assumptions. Empirical results show that the newly proposed prior on β improves accuracy for estimating the number of TFBS within a set of promoter sequences.

SPIC: A novel similarity metric for comparing transcription factor binding site motifs based on information contents

BackgroundDiscovering transcription factor binding sites (TFBS) is one of primary challenges to decipher complex gene regulatory networks encrypted in a genome. A set of short DNA sequences identified by a transcription factor (TF) is known as a motif, which can be expressed accurately in matrix form such as a position-specific scoring matrix (PSSM) and a position frequency matrix. Very frequently, we need to query a motif in a database of motifs by seeking its similar motifs, merge similar TFBS motifs possibly identified by the same TF, separate irrelevant motifs, or filter out spurious motifs. Therefore, a novel metric is required to seize slight differences between irrelevant motifs and highlight the similarity between motifs of the same group in all these applications. While there are already several metrics for motif similarity proposed before, their performance is still far from satisfactory for these applications.MethodsA novel metric has been proposed in this paper with name as SPIC (Similarity with Position Information Contents) for measuring the similarity between a column of a motif and a column of another motif. When defining this similarity score, we consider the likelihood that the column of the first motif's PFM can be produced by the column of the second motif's PSSM, and multiply the likelihood by the information content of the column of the second motif's PSSM, and vise versa. We evaluated the performance of SPIC combined with a local or a global alignment method having a function for affine gap penalty, for computing the similarity between two motifs. We also compared SPIC with seven existing state-of-the-arts metrics for their capability of clustering motifs from the same group and retrieving motifs from a database on three datasets.ResultsWhen used jointly with the Smith-Waterman local alignment method with an affine gap penalty function (gap open penalty is equal to1, gap extension penalty is equal to 0.5), SPIC outperforms the seven existing state-of-the-art motif similarity metrics combined with their best alignments for matching motifs in database searches, and clustering the same TF's sub-motifs or distinguishing relevant ones from a miscellaneous group of motifs.ConclusionsWe have developed a novel motif similarity metric that can more accurately match motifs in database searches, and more effectively cluster similar motifs and differentiate irrelevant motifs than do the other seven metrics we are aware of.

Molecular Characterization of Direct Target Genes and cis-Acting Consensus Recognized by Quorum-Sensing Regulator AphA in Vibrio parahaemolyticus

BackgroundAphA is the master quorum-sensing (QS) regulator operating at low cell density in vibrios. Molecular regulation of target genes by AphA has been characterized in Vibrio harveyi and V. cholerae, but it is still poorly understood in V. parahaemolyticus.Methodology/Principal FindingsThe AphA proteins are extremely conserved in V. parahaemolyticus, Vibrio sp. Ex25, Vibrio sp. EJY3, V. harveyi, V. vulnificus, V. splendidus, V. anguillarum, V. cholerae, and V. furnissii. The above nine AphA orthologs appear to recognize conserved cis-acting DNA signals which can be represented by two consensus constructs, a 20 bp box sequence and a position frequency matrix. V. parahaemolyticus AphA represses the transcription of ahpA, qrr4, and opaR through direct AphA-target promoter DNA association, while it inhibits the qrr2-3 transcription in an indirect manner. Translation and transcription starts, core promoter elements for sigma factor recognition, Shine-Dalgarno sequences for ribosome recognition, and AphA-binding sites (containing corresponding AphA box-like sequences) were determined for the three direct AphA targets ahpA, qrr4, and opaR in V. parahaemolyticus.Conclusions/SignificanceAphA-mediated repression of ahpA, qrr2-4, and opaR was characterized in V. parahaemolyticus by using multiple biochemical and molecular experiments. The computational promoter analysis indicated the conserved mechanism of transcriptional regulation of QS regulator-encoding genes ahpA, qrr4, and opaR in vibrios.

Transcriptional Regulation of opaR, qrr2–4 and aphA by the Master Quorum-Sensing Regulator OpaR in Vibrio parahaemolyticus

Background Vibrio parahaemolyticus is a leading cause of infectious diarrhea and enterogastritis via the fecal-oral route. V. harveyi is a pathogen of fishes and invertebrates, and has been used as a model for quorum sensing (QS) studies. LuxR is the master QS regulator (MQSR) of V. harveyi, and LuxR-dependent expression of its own gene, qrr2–4 and aphA have been established in V. harveyi. Molecular regulation of target genes by the V. parahaemolyticus MQSR OpaR is still poorly understood.Methodology/Principal FindingsThe bioinformatics analysis indicated that V. parahaemolyticus OpaR, V. harveyi LuxR, V. vulnificu SmcR, and V. alginolyticus ValR were extremely conserved, and that these four MQSRs appeared to recognize the same conserved cis-acting signals, which was represented by the consensus constructs manifesting as a position frequency matrix and as a 20 bp box, within their target promoters. The MQSR box-like sequences were found within the upstream DNA regions of opaR, qrr2–4 and aphA in V. parahaemolyticus, and the direct transcriptional regulation of these target genes by OpaR were further confirmed by multiple biochemical experiments including primer extension assay, gel mobility shift assay, and DNase I footprinting analysis. Translation and transcription starts, core promoter elements for sigma factor recognition, Shine-Dalgarno sequences for ribosome recognition, and OpaR-binding sites were determined for the five target genes of OpaR, which gave a structural map of the OpaR-dependent promoters. Further computational promoter analysis indicated the above regulatory circuits were shared by several other closely related Vibrios but with slight exceptions.Conclusions/SignificanceThis study gave a comprehensive computational and characterization of the direct transcriptional regulation of five target genes, opaR, qrr2–4 and ahpA, by OpaR in V. parahaemolyticus. These characterized regulatory circuits were conserved in V. harveyi and V. parahaemolyticus.

Compound Poisson Approximation of the Number of Occurrences of a Position Frequency Matrix (PFM) on Both Strands

Transcription factors play a key role in gene regulation by interacting with specific binding sites or motifs. Therefore, enrichment of binding motifs is important for genome annotation and efficient computation of the statistical significance, the p-value, of the enrichment of motifs is crucial. We propose an efficient approximation to compute the significance. Due to the incorporation of both strands of the DNA molecules and explicit modeling of dependencies between overlapping hits, we achieve accurate results for any DNA motif based on its Position Frequency Matrix (PFM) representation. The accuracy of the p-value approximation is shown by comparison with the simulated count distribution. Furthermore, we compare the approach with a binomial approximation, (compound) Poisson approximation, and a normal approximation. In general, our approach outperforms these approximations or is equally good but significantly faster. An implementation of our approach is available at http://mosta.molgen.mpg.de.

Natural similarity measures between position frequency matrices with an application to clustering

Transcription factors (TFs) play a key role in gene regulation by binding to target sequences. In silico prediction of potential binding of a TF to a binding site is a well-studied problem in computational biology. The binding sites for one TF are represented by a position frequency matrix (PFM). The discovery of new PFMs requires the comparison to known PFMs to avoid redundancies. In general, two PFMs are similar if they occur at overlapping positions under a null model. Still, most existing methods compute similarity according to probabilistic distances of the PFMs. Here we propose a natural similarity measure based on the asymptotic covariance between the number of PFM hits incorporating both strands. Furthermore, we introduce a second measure based on the same idea to cluster a set of the Jaspar PFMs. We show that the asymptotic covariance can be efficiently computed by a two dimensional convolution of the score distributions. The asymptotic covariance approach shows strong correlation with simulated data. It outperforms three alternative methods. The Jaspar clustering yields distinct groups of TFs of the same class. Furthermore, a representative PFM is given for each class. In contrast to most other clustering methods, PFMs with low similarity automatically remain singletons. A website to compute the similarity and to perform clustering, the source code and Supplementary Material are available at http://mosta.molgen.mpg.de.

Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm

This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences.