scholarly journals Cryo-EM structure of the nucleosome containing the ALB1 enhancer DNA sequence

Open Biology ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 170255 ◽  
Author(s):  
Yoshimasa Takizawa ◽  
Hiroki Tanaka ◽  
Shinichi Machida ◽  
Masako Koyama ◽  
Kazumitsu Maehara ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Precursor Cells ◽  
Dna Accessibility ◽  
Nucleosome Structure ◽  
Gene Enhancer ◽  
Pioneer Transcription Factor

Pioneer transcription factors specifically target their recognition DNA sequences within nucleosomes. FoxA is the pioneer transcription factor that binds to the ALB1 gene enhancer in liver precursor cells, and is required for liver differentiation in embryos. The ALB1 enhancer DNA sequence is reportedly incorporated into nucleosomes in cells, although the nucleosome structure containing the targeting sites for FoxA has not been clarified yet. In this study, we determined the nucleosome structure containing the ALB1 enhancer (N1) sequence, by cryogenic electron microscopy at 4.0 Å resolution. The nucleosome structure with the ALB1 enhancer DNA is not significantly different from the previously reported nucleosome structure with the Widom 601 DNA. Interestingly, in the nucleosomes, the ALB1 enhancer DNA contains local flexible regions, as compared to the Widom 601 DNA. Consistently, DNaseI treatments revealed that, in the nucleosome, the ALB1 enhancer (N1) DNA is more accessible than the Widom 601 sequence. The histones also associated less strongly with the ALB1 enhancer (N1) DNA than the Widom 601 DNA in the nucleosome. Therefore, the local histone–DNA contacts may be responsible for the enhanced DNA accessibility in the nucleosome with the ALB1 enhancer DNA.

scholarly journals Comparative structure analysis of the ETSi domain of ERG3 and its complex with the E74 promoter DNA sequence

2018 ◽  
Vol 74 (10) ◽  
pp. 656-663 ◽  
Author(s):  
Ruby Sharma ◽  
Shanti P. Gangwar ◽  
Ajay K. Saxena
Keyword(s):  
Transcription Factors ◽  
Dna Binding ◽  
Dna Sequence ◽  
Structure Analysis ◽  
Dna Sequences ◽  
Space Group ◽  
Class 1 ◽  
Comparative Structure ◽  
Ets Domain ◽  
Promoter Dna

ERG3 (ETS-related gene) is a member of the ETS (erythroblast transformation-specific) family of transcription factors, which contain a highly conserved DNA-binding domain. The ETS family of transcription factors differ in their binding to promoter DNA sequences, and the mechanism of their DNA-sequence discrimination is little known. In the current study, crystals of the ETSi domain (the ETS domain of ERG3 containing a CID motif) in space group P41212 and of its complex with the E74 DNA sequence (DNA9) in space group C2221 were obtained and their structures were determined. Comparative structure analysis of the ETSi domain and its complex with DNA9 with previously determined structures of the ERGi domain (the ETS domain of ERG containing inhibitory motifs) in space group P65212 and of the ERGi–DNA12 complex in space group P41212 were performed. The ETSi domain is observed as a homodimer in solution as well as in the crystallographic asymmetric unit. Superposition of the structure of the ETSi domain on that of the ERGi domain showed a major conformational change at the C-terminal DNA-binding autoinhibitory (CID) motif, while minor changes are observed in the loop regions of the ETSi-domain structure. The ETSi–DNA9 complex in space group C2221 forms a structure that is quite similar to that of the ERG–DNA12 complex in space group P41212. Upon superposition of the complexes, major conformational changes are observed at the 5′ and 3′ ends of DNA9, while the conformation of the core GGA nucleotides was quite conserved. Comparison of the ETSi–DNA9 structure with known structures of ETS class 1 protein–DNA complexes shows the similarities and differences in the promoter DNA binding and specificity of the class 1 ETS proteins.

scholarly journals The Pu.1 Locus Is Differentially Regulated at the Level of Chromatin Structure and Noncoding Transcription by Alternate Mechanisms at Distinct Developmental Stages of Hematopoiesis

2007 ◽  
Vol 27 (21) ◽  
pp. 7425-7438 ◽  
Author(s):  
Maarten Hoogenkamp ◽  
Hanna Krysinska ◽  
Richard Ingram ◽  
Gang Huang ◽  
Rachael Barlow ◽  
...  
Keyword(s):  
T Cells ◽  
Transcription Factor ◽  
Transcription Factors ◽  
B Cells ◽  
Regulatory Element ◽  
Precursor Cells ◽  
Hematopoietic Stem ◽  
Tissue Specific ◽  
Specific Regulation

ABSTRACT The Ets family transcription factor PU.1 is crucial for the regulation of hematopoietic development. Pu.1 is activated in hematopoietic stem cells and is expressed in mast cells, B cells, granulocytes, and macrophages but is switched off in T cells. Many of the transcription factors regulating Pu.1 have been identified, but little is known about how they organize Pu.1 chromatin in development. We analyzed the Pu.1 promoter and the upstream regulatory element (URE) using in vivo footprinting and chromatin immunoprecipitation assays. In B cells, Pu.1 was bound by a set of transcription factors different from that in myeloid cells and adopted alternative chromatin architectures. In T cells, Pu.1 chromatin at the URE was open and the same transcription factor binding sites were occupied as in B cells. The transcription factor RUNX1 was bound to the URE in precursor cells, but binding was down-regulated in maturing cells. In PU.1 knockout precursor cells, the Ets factor Fli-1 compensated for the lack of PU.1, and both proteins could occupy a subset of Pu.1 cis elements in PU.1-expressing cells. In addition, we identified novel URE-derived noncoding transcripts subject to tissue-specific regulation. Our results provide important insights into how overlapping, but different, sets of transcription factors program tissue-specific chromatin structures in the hematopoietic system.

scholarly journals Transcriptional Regulation in the G1-S Cell Cycle Stage in Fungi: Insights through Computational Analysis

2012 ◽  
Vol 6 (1) ◽  
pp. 43-54
Author(s):  
Viktor Martyanov ◽  
Robert H. Gross
Keyword(s):  
Cell Cycle ◽  
Transcription Factor ◽  
Transcriptional Regulation ◽  
Transcription Factors ◽  
Dna Sequences ◽  
Binding Sites ◽  
Positional Distribution ◽  
Upstream Sequence ◽  
Regulatory Pathways ◽  
Binding Factor

The transcription factor complexes Mlu1-box binding factor (MBF) and Swi4/6 cell cycle box binding factor (SBF) regulate the cell cycle in Saccharomyces cerevisiae. They activate hundreds of genes and are responsible for nor-mal cell cycle progression from G1 to S phase. We investigated the conservation of MBF and SBF binding sites during fungal evolution. Orthologs of S. cerevisiae targets of these transcription factors were identified in 37 fungal species and their upstream regions were analyzed for putative transcription factor binding sites. Both groups displayed enrichment in specific putative regulatory DNA sequences in their upstream regions and showed different preferred upstream motif loca-tions, variable patterns of evolutionary conservation of the motifs and enrichment in unique biological functions for the regulated genes. The results indicate that despite high sequence similarity of upstream DNA motifs putatively associated with G1-S transcriptional regulation by MBF and SBF transcription factors, there are important upstream sequence feature differences that may help differentiate the two seemingly similar regulatory modes. The incorporation of upstream motif sequence comparison, positional distribution and evolutionary variability of the motif can complement functional infor-mation about roles of the respective gene products and help elucidate transcriptional regulatory pathways and functions.

scholarly journals DeepGRN: Prediction of transcription factor binding site across cell-types using attention-based deep neural networks 

2021 ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding ◽  
Parallel Sequencing ◽  
Factor Binding Site ◽  
Factor Binding

Abstract BackgroundDue to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors.ResultsIn this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN.ConclusionsDeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

scholarly journals Nuclear microenvironments modulate transcription from low-affinity enhancers

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Albert Tsai ◽  
Anand K Muthusamy ◽  
Mariana RP Alves ◽  
Luke D Lavis ◽  
Robert H Singer ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Drosophila Melanogaster ◽  
Transcription Factors ◽  
Dna Sequences ◽  
Transcriptional Activation ◽  
Binding Sites ◽  
General Feature ◽  
Inverse Relationship ◽  
Chromosomal Location ◽  
Affinity Binding

Transcription factors bind low-affinity DNA sequences for only short durations. It is not clear how brief, low-affinity interactions can drive efficient transcription. Here, we report that the transcription factor Ultrabithorax (Ubx) utilizes low-affinity binding sites in the Drosophila melanogaster shavenbaby (svb) locus and related enhancers in nuclear microenvironments of high Ubx concentrations. Related enhancers colocalize to the same microenvironments independently of their chromosomal location, suggesting that microenvironments are highly differentiated transcription domains. Manipulating the affinity of svb enhancers revealed an inverse relationship between enhancer affinity and Ubx concentration required for transcriptional activation. The Ubx cofactor, Homothorax (Hth), was co-enriched with Ubx near enhancers that require Hth, even though Ubx and Hth did not co-localize throughout the nucleus. Thus, microenvironments of high local transcription factor and cofactor concentrations could help low-affinity sites overcome their kinetic inefficiency. Mechanisms that generate these microenvironments could be a general feature of eukaryotic transcriptional regulation.

scholarly journals DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding ◽  
Parallel Sequencing ◽  
Factor Binding Site ◽  
Factor Binding

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

scholarly journals LEAFY is a pioneer transcription factor and licenses cell reprogramming to floral fate

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Run Jin ◽  
Samantha Klasfeld ◽  
Yang Zhu ◽  
Meilin Fernandez Garcia ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Cell Fate ◽  
Chromatin Accessibility ◽  
Binding Motifs ◽  
Chromatin Remodelers ◽  
Pioneer Transcription Factor ◽  
Chromatin Reprogramming

AbstractMaster transcription factors reprogram cell fate in multicellular eukaryotes. Pioneer transcription factors have prominent roles in this process because of their ability to contact their cognate binding motifs in closed chromatin. Reprogramming is pervasive in plants, whose development is plastic and tuned by the environment, yet little is known about pioneer transcription factors in this kingdom. Here, we show that the master transcription factor LEAFY (LFY), which promotes floral fate through upregulation of the floral commitment factor APETALA1 (AP1), is a pioneer transcription factor. In vitro, LFY binds to the endogenous AP1 target locus DNA assembled into a nucleosome. In vivo, LFY associates with nucleosome occupied binding sites at the majority of its target loci, including AP1. Upon binding, LFY ‘unlocks’ chromatin locally by displacing the H1 linker histone and by recruiting SWI/SNF chromatin remodelers, but broad changes in chromatin accessibility occur later. Our study provides a mechanistic framework for patterning of inflorescence architecture and uncovers striking similarities between LFY and animal pioneer transcription factor.

scholarly journals LEAFY is a pioneer transcription factor and licenses cell reprogramming to floral fate

2020 ◽  
Author(s):  
Run Jin ◽  
Samantha Klasfeld ◽  
Meilin Fernandez Garcia ◽  
Jun Xiao ◽  
Soon-Ki Han ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Cell Fate ◽  
Chromatin Accessibility ◽  
Binding Motifs ◽  
Chromatin Remodelers ◽  
Pioneer Transcription Factor ◽  
Chromatin Reprogramming

ABSTRACTMaster transcription factors reprogram cell fate in multicellular eukaryotes. Pioneer transcription factors have prominent roles in this process because of their ability to contact their cognate binding motifs in closed chromatin. Reprogramming is pervasive in plants, whose development is plastic and tuned by the environment, yet no bonafide pioneer transcription factor has - been identified in this kingdom. Here we show that the master transcription factor LEAFY (LFY), which promotes floral fate through upregulation of the floral commitment factor APETALA1 (AP1), is a pioneer transcription factor. In vitro, LFY binds in a sequence-specific manner and with high affinity to the endogenous AP1 target locus DNA assembled into a nucleosome. In vivo, LFY associates with nucleosome occupied binding sites at the majority of its target loci, including AP1, where it co-occupies DNA with histones. Moreover, the LFY DNA contact helix shares defining properties with those of strong nucleosome binding pioneer factors. At the AP1 locus, LFY unlocks chromatin locally by displacing the H1 linker histone and by recruiting SWI/SNF chromatin remodelers, but broad changes in chromatin accessibility occur later and require activity of additional, non-pioneer transcription, factors. Our study provides a mechanistic framework for patterning of inflorescence architecture and uncovers striking similarities between plant and animal pioneer transcription factors. Further analyses aimed at elucidating the defining characteristics of pioneer transcription factors will allow harnessing these for enhanced cell fate reprogramming.

scholarly journals Prediction of transcription factor binding site across cell-types using attention-based deep neural networks

2020 ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding ◽  
Parallel Sequencing ◽  
Factor Binding Site ◽  
Factor Binding

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

scholarly journals Co-operation between the PAI and RED subdomains of Pax-8 in the interaction with the thyroglobulin promoter

1999 ◽  
Vol 337 (2) ◽  
pp. 253-262 ◽  
Author(s):  
Lucia PELLIZZARI ◽  
Gianluca TELL ◽  
Giuseppe DAMANTE
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Sequences ◽  
Target Genes ◽  
Dna Recognition ◽  
Cell Types ◽  
Full Length ◽  
Paired Domain ◽  
Binding Properties ◽  
Evolutionarily Conserved

Pax proteins are transcription factors that play an important role in the differentiation of several cell types. These proteins bind to specific DNA sequences through the paired domain. This evolutionarily conserved element is composed of two subdomains (PAI and RED), located at the N- and C-terminals, respectively. Due to the presence of these two subdomains, Pax proteins may recognize DNA in different modes, a possibility that has not been exhaustively explored yet. The C site of the thyroglobulin promoter is bound by the thyroid-specific transcription factor Pax-8. In this study we have characterized the mode by which the Pax-8 paired domain interacts with the C site. Results allow the identification of the respective positions of the PAI and RED subdomains when the full-length protein is bound to the C site. The binding of the isolated PAI and RED subdomains to the C site and to several related mutants was also evaluated. Both subdomains interact with DNA as a monomer and display a lower binding affinity than the full-length protein. Therefore, the Pax-8 paired domain–C site interaction occurs through a co-operation between the two subdomains. The binding properties of the PAI subdomain suggest that the co-operation between PAI and RED subdomains does not merely consist of the sum of contacts established by the single subdomain: the presence of the RED subdomain is necessary for correct DNA recognition by the PAI subdomain, thus accounting for a sort of chronology of events during DNA binding. Since the RED subdomain is much more variable than the PAI subdomain among Pax proteins, these results could explain how distinct Pax proteins may select different target genes.

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