scholarly journals Competition between histone and transcription factor binding regulates the onset of transcription in zebrafish embryos

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Shai R Joseph ◽  
Máté Pálfy ◽  
Lennart Hilbert ◽  
Mukesh Kumar ◽  
Jens Karschau ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Binding ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Zygotic Transcription ◽  
Maternal To Zygotic Transition ◽  
Nucleosome Density ◽  
Core Histones ◽  
Genome Activation

Upon fertilization, the genome of animal embryos remains transcriptionally inactive until the maternal-to-zygotic transition. At this time, the embryo takes control of its development and transcription begins. How the onset of zygotic transcription is regulated remains unclear. Here, we show that a dynamic competition for DNA binding between nucleosome-forming histones and transcription factors regulates zebrafish genome activation. Taking a quantitative approach, we found that the concentration of non-DNA-bound core histones sets the time for the onset of transcription. The reduction in nuclear histone concentration that coincides with genome activation does not affect nucleosome density on DNA, but allows transcription factors to compete successfully for DNA binding. In agreement with this, transcription factor binding is sensitive to histone levels and the concentration of transcription factors also affects the time of transcription. Our results demonstrate that the relative levels of histones and transcription factors regulate the onset of transcription in the embryo.

scholarly journals Competition between histone and transcription factor binding regulates the onset of transcription in zebrafish embryos

2017 ◽  
Author(s):  
Shai R. Joseph ◽  
Máté Pálfy ◽  
Lennart Hilbert ◽  
Mukesh Kumar ◽  
Jens Karschau ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Binding ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Zygotic Transcription ◽  
Maternal To Zygotic Transition ◽  
Nucleosome Density ◽  
Core Histones ◽  
Genome Activation

SUMMARYUpon fertilization, the genome of animal embryos remains transcriptionally inactive until the maternal-to-zygotic transition. At this time, the embryo takes control of its development and transcription begins. How the onset of zygotic transcription is regulated remains unclear. Here, we show that a dynamic competition for DNA binding between nucleosome-forming histones and transcription factors regulates zebrafish genome activation. Taking a quantitative approach, we found that the concentration of non-DNA bound core histones sets the time for the onset of transcription. The reduction in nuclear histone concentration that coincides with genome activation does not affect nucleosome density on DNA, but allows transcription factors to compete successfully for DNA binding. In agreement with this, transcription factor binding is sensitive to histone levels and the concentration of transcription factors also affects the time of transcription. Our results demonstrate that the relative levels of histones and transcription factors regulate the onset of transcription in the embryo.

scholarly journals Local DNA shape is a general principle of transcription factor binding specificity in Arabidopsis thaliana

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Janik Sielemann ◽  
Donat Wulf ◽  
Romy Schmidt ◽  
Andrea Bräutigam
Keyword(s):  
Machine Learning ◽  
Arabidopsis Thaliana ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Binding ◽  
Transcription Factor Binding ◽  
Binding Prediction ◽  
Factor Binding ◽  
A Genome ◽  
Dna Shape

AbstractUnderstanding gene expression will require understanding where regulatory factors bind genomic DNA. The frequently used sequence-based motifs of protein-DNA binding are not predictive, since a genome contains many more binding sites than are actually bound and transcription factors of the same family share similar DNA-binding motifs. Traditionally, these motifs only depict sequence but neglect DNA shape. Since shape may contribute non-linearly and combinational to binding, machine learning approaches ought to be able to better predict transcription factor binding. Here we show that a random forest machine learning approach, which incorporates the 3D-shape of DNA, enhances binding prediction for all 216 tested Arabidopsis thaliana transcription factors and improves the resolution of differential binding by transcription factor family members which share the same binding motif. We observed that DNA shape features were individually weighted for each transcription factor, even if they shared the same binding sequence.

Cytosine Arabinoside Substitution Decreases Transcription Factor–DNA Binding Element Complex Formation

2004 ◽  
Vol 128 (12) ◽  
pp. 1364-1371
Author(s):  
Ximbo Zhang ◽  
Frederick L. Kiechle
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Binding ◽  
Cytosine Arabinoside ◽  
Binding Sites ◽  
Binding Capacity ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Specific Transcription Factor ◽  
Factor Binding

Abstract Context.—The pyrimidine nucleoside analog, cytosine arabinoside (Ara-C), is an effective therapeutic agent for acute leukemia. The phosphorylated triphosphate, cytosine arabinoside triphosphate, competes with deoxycytosine triphosphate as a substrate for incorporation into DNA. Once incorporated into DNA, it inhibits DNA polymerase and topoisomerase I and modifies the tertiary structure of DNA. Objective.—To determine if the substitution of Ara-C for cytosine in double-stranded oligonucleotides that contain 4 specific transcription factor binding sites (TATA, GATA, C/EBP, and AP-2α) alters transcription factor binding to their respective DNA binding elements. Design.—Transcription factors were obtained from nuclear extracts from human promyelocytic leukemia HL-60 cells. [32P]-end-labeled double-stranded oligonucleotides that contained 1 or 2 specific transcription factor binding sites with or without Ara-C substitution for cytosine were used to assess transcription factor binding by electrophoretic mobility shift assay. Results.—The substitution of Ara-C for cytosine within and outside the transcription factor binding element (AP-2α, C/EBP), outside the binding element only (GATA, TATA), or within the binding element only (AP-2α) all result in a reduction in transcription factor binding to their respective DNA binding element. Conclusion.—The reduction of the binding capacity of transcription factors with their respective DNA binding elements may depend on structural changes within oligonucleotides induced by Ara-C incorporation. This altered binding capacity of transcription factors to their DNA binding elements may represent one mechanism for Ara-C cytotoxicity secondary to inhibition of transcription of new messenger RNAs and, subsequently, translation of new proteins.

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.

scholarly journals Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome

2018 ◽  
Author(s):  
Mehran Karimzadeh ◽  
Michael M. Hoffman
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Cell Types ◽  
Transcription Factor Binding ◽  
Regulatory Function ◽  
Factor Binding ◽  
Link Type ◽  
Genomic Regions ◽  
Factor Sequence

AbstractMotivationIdentifying transcription factor binding sites is the first step in pinpointing non-coding mutations that disrupt the regulatory function of transcription factors and promote disease. ChIP-seq is the most common method for identifying binding sites, but performing it on patient samples is hampered by the amount of available biological material and the cost of the experiment. Existing methods for computational prediction of regulatory elements primarily predict binding in genomic regions with sequence similarity to known transcription factor sequence preferences. This has limited efficacy since most binding sites do not resemble known transcription factor sequence motifs, and many transcription factors are not even sequence-specific.ResultsWe developed Virtual ChIP-seq, which predicts binding of individual transcription factors in new cell types using an artificial neural network that integrates ChIP-seq results from other cell types and chromatin accessibility data in the new cell type. Virtual ChIP-seq also uses learned associations between gene expression and transcription factor binding at specific genomic regions. This approach outperforms methods that predict TF binding solely based on sequence preference, pre-dicting binding for 36 transcription factors (Matthews correlation coefficient > 0.3).AvailabilityThe datasets we used for training and validation are available at https://virchip.hoffmanlab.org. We have deposited in Zenodo the current version of our software (http://doi.org/10.5281/zenodo.1066928), datasets (http://doi.org/10.5281/zenodo.823297), predictions for 36 transcription factors on Roadmap Epigenomics cell types (http://doi.org/10.5281/zenodo.1455759), and predictions in Cistrome as well as ENCODE-DREAM in vivo TF Binding Site Prediction Challenge (http://doi.org/10.5281/zenodo.1209308).

scholarly journals In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites

2016 ◽  
Vol 2016 ◽  
pp. 1-27 ◽  
Author(s):  
Kristopher J. L. Irizarry ◽  
Randall L. Bryden
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Coat Color ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Color Variation ◽  
Factor Binding ◽  
Conserved Sequence ◽  
Evolutionarily Conserved

Color variation provides the opportunity to investigate the genetic basis of evolution and selection. Reptiles are less studied than mammals. Comparative genomics approaches allow for knowledge gained in one species to be leveraged for use in another species. We describe a comparative vertebrate analysis of conserved regulatory modules in pythons aimed at assessing bioinformatics evidence that transcription factors important in mammalian pigmentation phenotypes may also be important in python pigmentation phenotypes. We identified 23 python orthologs of mammalian genes associated with variation in coat color phenotypes for which we assessed the extent of pairwise protein sequence identity between pythons and mouse, dog, horse, cow, chicken, anole lizard, and garter snake. We next identified a set of melanocyte/pigment associated transcription factors (CREB, FOXD3, LEF-1, MITF, POU3F2, and USF-1) that exhibit relatively conserved sequence similarity within their DNA binding regions across species based on orthologous alignments across multiple species. Finally, we identified 27 evolutionarily conserved clusters of transcription factor binding sites within ~200-nucleotide intervals of the 1500-nucleotide upstream regions of AIM1, DCT, MC1R, MITF, MLANA, OA1, PMEL, RAB27A, and TYR from Python bivittatus. Our results provide insight into pigment phenotypes in pythons.

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 ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Mette Bentsen ◽  
Philipp Goymann ◽  
Hendrik Schultheis ◽  
Kathrin Klee ◽  
Anastasiia Petrova ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factor Binding ◽  
Zygotic Genome Activation ◽  
Factor Binding ◽  
Genome Activation ◽  
Kinetics Of ◽  
Zygotic Genome
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