scholarly journals Differential gene regulation in DAPT-treated Hydra reveals candidate direct notch signalling targets

2021 ◽  
Author(s):  
Jasmin Moneer ◽  
Stefan Siebert ◽  
Stefan Krebs ◽  
Jack Cazet ◽  
Andrea Prexl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Expression Patterns ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Notch Signalling ◽  
Data Set ◽  
Regulatory Regions ◽  
Factor Binding

In Hydra, Notch inhibition causes defects in head patterning and prevents differentiation of proliferating nematocyte progenitor cells into mature nematocytes. To understand the molecular mechanisms by which the Notch pathway regulates these processes we performed RNAseq and identified genes that are differentially regulated in response to 48 hours of treating the animals with the Notch-inhibitor DAPT. To identify candidate direct regulators of Notch-signalling, we profiled gene expression changes that occur during subsequent restoration of Notch-activity and performed promoter analyses to identify RBPJ transcription factor binding sites in the regulatory regions of Notch-responsive genes. Interrogating the available single cell sequencing data set revealed gene expression patterns of Notch-regulated Hydra genes. By these analyses a comprehensive picture of the molecular pathways regulated by Notch signalling in head patterning and in interstitial cell differentiation in Hydra emerged. As prime candidates for direct Notch-target genes, in addition to HyHes, we suggest Sp5 and HyAlx. They rapidly recovered their expression levels after DAPT removal and possess Notch-responsive RBPJ transcription factor binding sites in their regulatory regions.

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 CiiiDER: a new tool for predicting and analysing transcription factor binding sites

2019 ◽  
Author(s):  
Linden J. Gearing ◽  
Helen E. Cumming ◽  
Ross Chapman ◽  
Alexander M. Finkel ◽  
Isaac B. Woodhouse ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Transcription Factor Binding Sites ◽  
Computational Prediction ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Java Programming ◽  
Computer Operating System

AbstractThe availability of large amounts of high-throughput genomic, transcriptomic and epigenomic data has provided opportunity to understand regulation of the cellular transcriptome with an unprecedented level of detail. As a result, research has advanced from identifying gene expression patterns associated with particular conditions to elucidating signalling pathways that regulate expression. There are over 1,000 transcription factors (TFs) in vertebrates that play a role in this regulation. Determining which of these are likely to be controlling a set of genes can be assisted by computational prediction, utilising experimentally verified binding site motifs.Here we present CiiiDER, an integrated computational toolkit for transcription factor binding analysis, written in the Java programming language, to make it independent of computer operating system. It is operated through an intuitive graphical user interface with interactive, high-quality visual outputs, making it accessible to all researchers. CiiiDER predicts transcription factor binding sites (TFBSs) across regulatory regions of interest, such as promoters and enhancers derived from any species. It can perform an enrichment analysis to identify TFs that are significantly over- or under-represented in comparison to a bespoke background set and thereby elucidate pathways regulating sets of genes of pathophysiological importance.CiiiDER is available from www.ciiider.org.

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.

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