scholarly journals Altered Transcription Factor Binding and Gene Bivalency in Islets of Intrauterine Growth Retarded Rats

Cells ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1435
Author(s):  
Yu-Chin Lien ◽  
Paul Zhiping Wang ◽  
Xueqing Maggie Lu ◽  
Rebecca A. Simmons
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Transcription Factor Binding ◽  
Binding Motifs ◽  
Factor Binding ◽  
Intrauterine Growth ◽  
Gene Dysregulation ◽  
Genome Wide

Intrauterine growth retardation (IUGR), which induces epigenetic modifications and permanent changes in gene expression, has been associated with the development of type 2 diabetes. Using a rat model of IUGR, we performed ChIP-Seq to identify and map genome-wide histone modifications and gene dysregulation in islets from 2- and 10-week rats. IUGR induced significant changes in the enrichment of H3K4me3, H3K27me3, and H3K27Ac marks in both 2-wk and 10-wk islets, which were correlated with expression changes of multiple genes critical for islet function in IUGR islets. ChIP-Seq analysis showed that IUGR-induced histone mark changes were enriched at critical transcription factor binding motifs, such as C/EBPs, Ets1, Bcl6, Thrb, Ebf1, Sox9, and Mitf. These transcription factors were also identified as top upstream regulators in our previously published transcriptome study. In addition, our ChIP-seq data revealed more than 1000 potential bivalent genes as identified by enrichment of both H3K4me3 and H3K27me3. The poised state of many potential bivalent genes was altered by IUGR, particularly Acod1, Fgf21, Serpina11, Cdh16, Lrrc27, and Lrrc66, key islet genes. Collectively, our findings suggest alterations of histone modification in key transcription factors and genes that may contribute to long-term gene dysregulation and an abnormal islet phenotype in IUGR rats.

scholarly journals Genome-wide use of high- and low-affinity Tbrain transcription factor binding sites during echinoderm development

2017 ◽  
Vol 114 (23) ◽  
pp. 5854-5861 ◽  
Author(s):  
Gregory A. Cary ◽  
Alys M. Cheatle Jarvela ◽  
Rene D. Francolini ◽  
Veronica F. Hinman
Keyword(s):  
Transcription Factor ◽  
Sea Urchins ◽  
Transcription Factor Binding ◽  
Model Systems ◽  
Binding Motif ◽  
Regulatory Sequences ◽  
Binding Motifs ◽  
Chip Sequencing ◽  
Factor Binding ◽  
Genome Wide

Sea stars and sea urchins are model systems for interrogating the types of deep evolutionary changes that have restructured developmental gene regulatory networks (GRNs). Although cis-regulatory DNA evolution is likely the predominant mechanism of change, it was recently shown that Tbrain, a Tbox transcription factor protein, has evolved a changed preference for a low-affinity, secondary binding motif. The primary, high-affinity motif is conserved. To date, however, no genome-wide comparisons have been performed to provide an unbiased assessment of the evolution of GRNs between these taxa, and no study has attempted to determine the interplay between transcription factor binding motif evolution and GRN topology. The study here measures genome-wide binding of Tbrain orthologs by using ChIP-sequencing and associates these orthologs with putative target genes to assess global function. Targets of both factors are enriched for other regulatory genes, although nonoverlapping sets of functional enrichments in the two datasets suggest a much diverged function. The number of low-affinity binding motifs is significantly depressed in sea urchins compared with sea star, but both motif types are associated with genes from a range of functional categories. Only a small fraction (∼10%) of genes are predicted to be orthologous targets. Collectively, these data indicate that Tbr has evolved significantly different developmental roles in these echinoderms and that the targets and the binding motifs in associated cis-regulatory sequences are dispersed throughout the hierarchy of the GRN, rather than being biased toward terminal process or discrete functional blocks, which suggests extensive evolutionary tinkering.

scholarly journals A single ChIP-seq dataset is sufficient for comprehensive analysis of motifs co-occurrence with MCOT package

2019 ◽  
Vol 47 (21) ◽  
pp. e139-e139 ◽  
Author(s):  
Victor Levitsky ◽  
Elena Zemlyanskaya ◽  
Dmitry Oshchepkov ◽  
Olga Podkolodnaya ◽  
Elena Ignatieva ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Fold Increase ◽  
Transcription Factor Binding ◽  
Single Chip ◽  
Maximum Information ◽  
Factor Binding ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Genome Wide Data

Abstract Recognition of composite elements consisting of two transcription factor binding sites gets behind the studies of tissue-, stage- and condition-specific transcription. Genome-wide data on transcription factor binding generated with ChIP-seq method facilitate an identification of composite elements, but the existing bioinformatics tools either require ChIP-seq datasets for both partner transcription factors, or omit composite elements with motifs overlapping. Here we present an universal Motifs Co-Occurrence Tool (MCOT) that retrieves maximum information about overrepresented composite elements from a single ChIP-seq dataset. This includes homo- and heterotypic composite elements of four mutual orientations of motifs, separated with a spacer or overlapping, even if recognition of motifs within composite element requires various stringencies. Analysis of 52 ChIP-seq datasets for 18 human transcription factors confirmed that for over 60% of analyzed datasets and transcription factors predicted co-occurrence of motifs implied experimentally proven protein-protein interaction of respecting transcription factors. Analysis of 164 ChIP-seq datasets for 57 mammalian transcription factors showed that abundance of predicted composite elements with an overlap of motifs compared to those with a spacer more than doubled; and they had 1.5-fold increase of asymmetrical pairs of motifs with one more conservative ‘leading’ motif and another one ‘guided’.

scholarly journals Genome-wide conserved consensus transcription factor binding motifs are hyper-methylated

BMC Genomics ◽  
2010 ◽  
Vol 11 (1) ◽  
pp. 519 ◽  
Author(s):  
Mun-Kit Choy ◽  
Mehregan Movassagh ◽  
Hock-Guan Goh ◽  
Martin R Bennett ◽  
Thomas A Down ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factor Binding ◽  
Binding Motifs ◽  
Factor Binding ◽  
Genome Wide ◽  
Transcription Factor Binding Motifs

scholarly journals An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Divyanshi Srivastava ◽  
Begüm Aydin ◽  
Esteban O. Mazzoni ◽  
Shaun Mahony
Keyword(s):  
Neural Network ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Binding ◽  
Dna Sequences ◽  
Binding Specificity ◽  
Transcription Factor Binding ◽  
Cell Type ◽  
Factor Binding ◽  
Genome Wide

Abstract Background Transcription factor (TF) binding specificity is determined via a complex interplay between the transcription factor’s DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with transcription factor binding in a given cell type have been well characterized. For instance, the binding sites for a majority of transcription factors display concurrent chromatin accessibility. However, concurrent chromatin features reflect the binding activities of the transcription factor itself and thus provide limited insight into how genome-wide TF-DNA binding patterns became established in the first place. To understand the determinants of transcription factor binding specificity, we therefore need to examine how newly activated transcription factors interact with sequence and preexisting chromatin landscapes. Results Here, we investigate the sequence and preexisting chromatin predictors of TF-DNA binding by examining the genome-wide occupancy of transcription factors that have been induced in well-characterized chromatin environments. We develop Bichrom, a bimodal neural network that jointly models sequence and preexisting chromatin data to interpret the genome-wide binding patterns of induced transcription factors. We find that the preexisting chromatin landscape is a differential global predictor of TF-DNA binding; incorporating preexisting chromatin features improves our ability to explain the binding specificity of some transcription factors substantially, but not others. Furthermore, by analyzing site-level predictors, we show that transcription factor binding in previously inaccessible chromatin tends to correspond to the presence of more favorable cognate DNA sequences. Conclusions Bichrom thus provides a framework for modeling, interpreting, and visualizing the joint sequence and chromatin landscapes that determine TF-DNA binding dynamics.

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