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 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 GRID-seq assisted prediction of transcription factor binding motifs

2018 ◽  
Author(s):  
Werner Pieter Veldsman
Keyword(s):  
Transcription Factor ◽  
Deep Learning ◽  
Rna Sequencing ◽  
Transcription Factor Binding ◽  
Binding Motif ◽  
Functional Protein ◽  
Binding Motifs ◽  
Factor Binding ◽  
Dnase Hypersensitivity ◽  
Transcription Factor Binding Motifs

AbstractExperimental validation of computationally predicted transcription factor binding motifs is desirable. Increased RNA levels in the vicinity of predicted protein-chromosomal binding motifs intuitively suggest regulatory activity. With this intuition in mind, the approach presented here juxtaposes publicly available experimentally derived GRID-seq data with binding motif predictions computationally determined by deep learning models. The aim is to demonstrate the feasibility of using RNA-sequencing data to improve binding motif prediction accuracy. Publicly available GRID-seq scores and computed DeepBind scores could be aggregated by chromosomal region and anomalies within the aggregated data could be detected using mahalanobis distance analysis. A mantel’s test of matrices containing pairwise hamming distances showed significant differences between 1) randomly ranked sequences, 2) sequences ranked by non-GRID-seq assisted scores, and 3) sequences ranked by GRID-seq assisted scores. Plots of mahalanobis ranked binding motifs revealed an inversely proportional relationship between GRID-seq scores and DeepBind scores. Data points with high DeepBind scores but low GRID-seq scores had no DNAse hypersensitivity clusters annotated to their respective sequences. However, DNase hypersensitivity was observed for high scoring DeepBind motifs with moderate GRID-seq scores. Binding motifs of interest were recognized by their deviance from the inversely proportional tendency, and the underlying context sequences of these predicted motifs were on occasion associated with DNAse hypersensitivity unlike the most highly ranked motif scores when DeepBind was used in isolation. This article presents a novel combinatory approach to predict functional protein-chromosomal binding motifs. The two underlying methods are based on recent developments in the fields of RNA sequencing and deep learning, respectively. They are shown to be suited for synergistic use, which broadens the scope of their respective applications.

scholarly journals MoRAine - A web server for fast computational transcription factor binding motif re-annotation

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Jan Baumbach ◽  
Tobias Wittkop ◽  
Jochen Weile ◽  
Thomas Kohl ◽  
Sven Rahmann
Keyword(s):  
Transcription Factor ◽  
Information Content ◽  
Web Server ◽  
Transcription Factor Binding ◽  
Classification Performance ◽  
Binding Motif ◽  
Binding Motifs ◽  
Factor Binding ◽  
Sequence Logos ◽  
Transcription Factor Binding Motifs

SummaryBackground: A precise experimental identification of transcription factor binding motifs (TFBMs), accurate to a single base pair, is time-consuming and difficult. For several databases, TFBM annotations are extracted from the literature and stored 5ʹ → 3ʹ relative to the target gene. Mixing the two possible orientations of a motif results in poor information content of subsequently computed position frequency matrices (PFMs) and sequence logos. Since these PFMs are used to predict further TFBMs, we address the question if the TFBMs underlying a PFM can be re-annotated automatically to improve both the information content of the PFM and subsequent classification performance.Results: We present MoRAine, an algorithm that re-annotates transcription factor binding motifs. Each motif with experimental evidence underlying a PFM is compared against each other such motif. The goal is to re-annotate TFBMs by possibly switching their strands and shifting them a few positions in order to maximize the information content of the resulting adjusted PFM. We present two heuristic strategies to perform this optimization and subsequently show that MoRAine significantly improves the corresponding sequence logos. Furthermore, we justify the method by evaluating specificity, sensitivity, true positive, and false positive rates of PFM-based TFBM predictions for E. coli using the original database motifs and the MoRAine-adjusted motifs. The classification performance is considerably increased if MoRAine is used as a preprocessing step.Conclusions: MoRAine is integrated into a publicly available web server and can be used online or downloaded as a stand-alone version from http://moraine.cebitec.uni-bielefeld.de.

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 Subtype-associated epigenomic landscape and 3D genome structure in bladder cancer

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tejaswi Iyyanki ◽  
Baozhen Zhang ◽  
Qixuan Wang ◽  
Ye Hou ◽  
Qiushi Jin ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Transcription Factor ◽  
Cancer Cell ◽  
Genome Structure ◽  
Transcription Factor Binding ◽  
Specific Gene ◽  
Open Chromatin ◽  
Factor Binding ◽  
3D Genome ◽  
Genome Wide

Abstract Muscle-invasive bladder cancers are characterized by their distinct expression of luminal and basal genes, which could be used to predict key clinical features such as disease progression and overall survival. Transcriptionally, FOXA1, GATA3, and PPARG are shown to be essential for luminal subtype-specific gene regulation and subtype switching, while TP63, STAT3, and TFAP2 family members are critical for regulation of basal subtype-specific genes. Despite these advances, the underlying epigenetic mechanisms and 3D chromatin architecture responsible for subtype-specific regulation in bladder cancer remain unknown. Result We determine the genome-wide transcriptome, enhancer landscape, and transcription factor binding profiles of FOXA1 and GATA3 in luminal and basal subtypes of bladder cancer. Furthermore, we report the first-ever mapping of genome-wide chromatin interactions by Hi-C in both bladder cancer cell lines and primary patient tumors. We show that subtype-specific transcription is accompanied by specific open chromatin and epigenomic marks, at least partially driven by distinct transcription factor binding at distal enhancers of luminal and basal bladder cancers. Finally, we identify a novel clinically relevant transcription factor, Neuronal PAS Domain Protein 2 (NPAS2), in luminal bladder cancers that regulates other subtype-specific genes and influences cancer cell proliferation and migration. Conclusion In summary, our work identifies unique epigenomic signatures and 3D genome structures in luminal and basal urinary bladder cancers and suggests a novel link between the circadian transcription factor NPAS2 and a clinical bladder cancer subtype.

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