scholarly journals Homotypic clusters of transcription factor binding sites: A model system for understanding the physical mechanics of gene expression

2014 ◽  
Vol 10 (17) ◽  
pp. 63-69 ◽  
Author(s):  
Daphne Ezer ◽  
Nicolae Radu Zabet ◽  
Boris Adryan
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Model System ◽  
Factor Binding ◽  
Physical Mechanics

scholarly journals Bioinformatics Analysis of SNPs in IL-6 Gene Promoter of Jinghai Yellow Chickens

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 446 ◽  
Author(s):  
Shijie Xin ◽  
Xiaohui Wang ◽  
Guojun Dai ◽  
Jingjing Zhang ◽  
Tingting An ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Promoter Region ◽  
Bioinformatics Analysis ◽  
Cpg Islands ◽  
Regulatory Region ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding

The proinflammatory cytokine, interleukin-6 (IL-6), plays a critical role in many chronic inflammatory diseases, particularly inflammatory bowel disease. To investigate the regulation of IL-6 gene expression at the molecular level, genomic DNA sequencing of Jinghai yellow chickens (Gallus gallus) was performed to detect single-nucleotide polymorphisms (SNPs) in the region −2200 base pairs (bp) upstream to 500 bp downstream of IL-6. Transcription factor binding sites and CpG islands in the IL-6 promoter region were predicted using bioinformatics software. Twenty-eight SNP sites were identified in IL-6. Four of these 28 SNPs, three [−357 (G > A), −447 (C > G), and −663 (A > G)] in the 5′ regulatory region and one in the 3′ non-coding region [3177 (C > T)] are not labelled in GenBank. Bioinformatics analysis revealed 11 SNPs within the promoter region that altered putative transcription factor binding sites. Furthermore, the C-939G mutation in the promoter region may change the number of CpG islands, and SNPs in the 5′ regulatory region may influence IL-6 gene expression by altering transcription factor binding or CpG methylation status. Genetic diversity analysis revealed that the newly discovered A-663G site significantly deviated from Hardy-Weinberg equilibrium. These results provide a basis for further exploration of the promoter function of the IL-6 gene and the relationships of these SNPs to intestinal inflammation resistance in chickens.

scholarly journals Pleiotropy in enhancer function is encoded through diverse genetic architectures

2017 ◽  
Author(s):  
Ella Preger-Ben Noon ◽  
Gonzalo Sabarís ◽  
Daniela Ortiz ◽  
Jonathan Sager ◽  
Anna Liebowitz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Developmental Stages ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Drive Gene Expression ◽  
Enhancer Function ◽  
Drive Gene

AbstractDevelopmental genes can have complex c/s-regulatory regions, with multiple enhancers scattered across stretches of DNA spanning tens or hundreds of kilobases. Early work revealed remarkable modularity of enhancers, where distinct regions of DNA, bound by combinations of transcription factors, drive gene expression in defined spatio-temporal domains. Nevertheless, a few reports have shown that enhancer function may be required in multiple developmental stages, implying that regulatory elements can be pleiotropic. In these cases, it is not clear whether the pleiotropic enhancers employ the same transcription factor binding sites to drive expression at multiple developmental stages or whether enhancers function as chromatin scaffolds, where independent sets of transcription factor binding sites act at different stages. In this work we have studied the activity of the enhancers of the shavenbaby gene throughout D. melanogaster development. We found that all seven shavenbaby enhancers drive gene expression in multiple tissues and developmental stages at varying levels of redundancy. We have explored how this pleiotropy is encoded in two of these enhancers. In one enhancer, the same transcription factor binding sites contribute to embryonic and pupal expression, whereas for a second enhancer, these roles are largely encoded by distinct transcription factor binding sites. Our data suggest that enhancer pleiotropy might be a common feature of c/s-regulatory regions of developmental genes and that this pleiotropy can be encoded through multiple genetic architectures.

CLUSTERING AND RE-CLUSTERING FOR PATTERN DISCOVERY IN GENE EXPRESSION DATA

2005 ◽  
Vol 03 (02) ◽  
pp. 281-301 ◽  
Author(s):  
PATRICK C. H. MA ◽  
KEITH C. C. CHAN ◽  
DAVID K. Y. CHIU
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Gene Expression Data ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Pairwise Distance ◽  
Expression Data ◽  
Promoter Regions ◽  
Factor Binding

The combined interpretation of gene expression data and gene sequences is important for the investigation of the intricate relationships of gene expression at the transcription level. The expression data produced by microarray hybridization experiments can lead to the identification of clusters of co-expressed genes that are likely co-regulated by the same regulatory mechanisms. By analyzing the promoter regions of co-expressed genes, the common regulatory patterns characterized by transcription factor binding sites can be revealed. Many clustering algorithms have been used to uncover inherent clusters in gene expression data. In this paper, based on experiments using simulated and real data, we show that the performance of these algorithms could be further improved. For the clustering of expression data typically characterized by a lot of noise, we propose to use a two-phase clustering algorithm consisting of an initial clustering phase and a second re-clustering phase. The proposed algorithm has several desirable features: (i) it utilizes both local and global information by computing both a "local" pairwise distance between two gene expression profiles in Phase 1 and a "global" probabilistic measure of interestingness of cluster patterns in Phase 2, (ii) it distinguishes between relevant and irrelevant expression values when performing re-clustering, and (iii) it makes explicit the patterns discovered in each cluster for possible interpretations. Experimental results show that the proposed algorithm can be an effective algorithm for discovering clusters in the presence of very noisy data. The patterns that are discovered in each cluster are found to be meaningful and statistically significant, and cannot otherwise be easily discovered. Based on these discovered patterns, genes co-expressed under the same experimental conditions and range of expression levels have been identified and evaluated. When identifying regulatory patterns at the promoter regions of the co-expressed genes, we also discovered well-known transcription factor binding sites in them. These binding sites can provide explanations for the co-expressed patterns.

scholarly journals Regulation of chicken vanin1 gene expression by peroxisome proliferators activated receptor α and miRNA-181a-5p

2021 ◽  
Vol 34 (2) ◽  
pp. 172-184
Author(s):  
Zhongliang Wang ◽  
Jianfeng Yu ◽  
Nan Hua ◽  
Jie Li ◽  
Lu Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Promoter Region ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Expression Level ◽  
Luciferase Reporter ◽  
Peroxisome Proliferators ◽  
Factor Binding

Objective: Vanin1 (VNN1) is a pantetheinase that can catalyze the hydrolysis of pantetheine to produce pantothenic acid and cysteamine. Our previous studies showed that <i>VNN1</i> is specifically expressed in chicken liver. In this study, we aimed to investigate the roles of peroxisome proliferators activated receptor α (PPARα) and miRNA-181a-5p in regulating <i>VNN1</i> gene expression in chicken liver.Methods: 5′-RACE was performed to identify the transcription start site of chicken <i>VNN1</i>. JASPAR and TFSEARCH were used to analyze the potential transcription factor binding sites in the promoter region of chicken <i>VNN1</i> and miRanda was used to search miRNA binding sites in 3′ untranslated region (3′UTR) of chicken <i>VNN1</i>. We used a knock-down strategy to manipulate PPARα (or miRNA-181a-5p) expression levels <i>in vitro</i> to further investigate its effect on <i>VNN1</i> gene transcription. Luciferase reporter assays were used to explore the specific regions of VNN1 targeted by PPARα and miRNA-181a-5p.Results: Sequence analysis of the VNN1 promoter region revealed several transcription factor-binding sites, including hepatocyte nuclear factor 1α (HNF1α), PPARα, and CCAAT/enhancer binding protein α. GW7647 (a specific agonist of PPARα) increased the expression level of <i>VNN1</i> mRNA in chicken primary hepatocytes, whereas knockdown of PPARα with siRNA increased VNN1 mRNA expression. Moreover, the predicted PPARα-binding site was confirmed to be necessary for PPARα regulation of <i>VNN1</i> gene expression. In addition, the <i>VNN1</i> 3′UTR contains a sequence that is completely complementary to nucleotides 1 to 7 of miRNA-181a-5p. Overexpression of miR-181a-5p significantly decreased the expression level of <i>VNN1</i> mRNA.Conclusion: This study demonstrates that PPARα is an important transcriptional activator of <i>VNN1</i> gene expression and that miRNA-181a-5p acts as a negative regulator of <i>VNN1</i> expression in chicken hepatocytes.

Prioritize Transcription Factor Binding Sites for Multiple Co-Expressed Gene Sets Based on Lasso Multinomial Regression Models

2016 ◽  
pp. 280-315 ◽  
Author(s):  
Hong Hu ◽  
Yang Dai
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Regression Models ◽  
Expression Profiles ◽  
Transcription Factor Binding Sites ◽  
Computational Prediction ◽  
Transcription Factor Binding ◽  
Multinomial Regression ◽  
Factor Binding

Computational prediction of cis-regulatory elements for a set of co-expressed genes based on sequence analysis provides an overwhelming volume of potential transcription factor binding sites. It presents a challenge to prioritize a set of functional transcription factors and their binding sites on the regulatory regions of the target genes that are relevant to the gene expression study. A novel approach based on the use of lasso multinomial regression models is proposed to address this problem. We examine the ability of the lasso models using a time-course microarray data obtained from a comprehensive study of gene expression profiles in skin and mucosal in mouse over all stages of wound healing.

Prioritize Transcription Factor Binding Sites for Multiple Co-Expressed Gene Sets Based on Lasso Multinomial Regression Models

Biotechnology ◽  
2019 ◽  
pp. 940-968
Author(s):  
Hong Hu ◽  
Yang Dai
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Regression Models ◽  
Expression Profiles ◽  
Transcription Factor Binding Sites ◽  
Computational Prediction ◽  
Transcription Factor Binding ◽  
Multinomial Regression ◽  
Factor Binding

Computational prediction of cis-regulatory elements for a set of co-expressed genes based on sequence analysis provides an overwhelming volume of potential transcription factor binding sites. It presents a challenge to prioritize a set of functional transcription factors and their binding sites on the regulatory regions of the target genes that are relevant to the gene expression study. A novel approach based on the use of lasso multinomial regression models is proposed to address this problem. We examine the ability of the lasso models using a time-course microarray data obtained from a comprehensive study of gene expression profiles in skin and mucosal in mouse over all stages of wound healing.

scholarly journals Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

2021 ◽  
Vol 22 (12) ◽  
pp. 6454
Author(s):  
Arina O. Degtyareva ◽  
Elena V. Antontseva ◽  
Tatiana I. Merkulova
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Genetic Variants ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Genome Wide Association Studies ◽  
Factor Binding ◽  
Genome Wide ◽  
Regulatory Snps

The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

scholarly journals Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Guohua Wang ◽  
Fang Wang ◽  
Qian Huang ◽  
Yu Li ◽  
Yunlong Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Interferon Treatment ◽  
Factor Binding ◽  
Hypersensitive Sites

Transcription factors are proteins that bind to DNA sequences to regulate gene transcription. The transcription factor binding sites are short DNA sequences (5–20 bp long) specifically bound by one or more transcription factors. The identification of transcription factor binding sites and prediction of their function continue to be challenging problems in computational biology. In this study, by integrating the DNase I hypersensitive sites with known position weight matrices in the TRANSFAC database, the transcription factor binding sites in gene regulatory region are identified. Based on the global gene expression patterns in cervical cancer HeLaS3 cell and HelaS3-ifnα4h cell (interferon treatment on HeLaS3 cell for 4 hours), we present a model-based computational approach to predict a set of transcription factors that potentially cause such differential gene expression. Significantly, 6 out 10 predicted functional factors, including IRF, IRF-2, IRF-9, IRF-1 and IRF-3, ICSBP, belong to interferon regulatory factor family and upregulate the gene expression levels responding to the interferon treatment. Another factor, ISGF-3, is also a transcriptional activator induced by interferon alpha. Using the different transcription factor binding sites selected criteria, the prediction result of our model is consistent. Our model demonstrated the potential to computationally identify the functional transcription factors in gene regulation.

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