scholarly journals Long-term association of a transcription factor with its chromatin binding site can stabilize gene expression and cell fate commitment

2020 ◽  
Vol 117 (26) ◽  
pp. 15075-15084 ◽  
Author(s):  
J. B. Gurdon ◽  
Khayam Javed ◽  
Munender Vodnala ◽  
Nigel Garrett
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Residence Time ◽  
Transcription Factor Binding ◽  
Oocyte Nucleus ◽  
Chromatin Binding ◽  
Chromosomal Dna ◽  
Factor Binding

Some lineage-determining transcription factors are overwhelmingly important in directing embryonic cells to a particular differentiation pathway, such asAscl1for nerve. They also have an exceptionally strong ability to force cells to change from an unrelated pathway to one preferred by their action. Transcription factors are believed to have a very short residence time of only a few seconds on their specific DNA or chromatin-binding sites. We have developed a procedure in which DNA containing one copy of the binding site for the neural-inducing factorAscl1is injected directly into aXenopusoocyte nucleus which has been preloaded with a limiting amount of theAscl1transcription factor protein. This is followed by a further injection of DNA as a competitor, either in a plasmid or in chromosomal DNA, containing the same binding site but with a different reporter. Importantly, expression of the reporter provides a measure of the function of the transcription factor in addition to its residence time. The same long residence time and resistance to competition are seen with the estrogen receptor and its DNA response elements. We find that in this nondividing oocyte, the nerve-inducing factorAscl1can remain bound to a specific chromatin site for hours or days and thereby help to stabilize gene expression. This stability of transcription factor binding to chromatin is a necessary part of its action because removal of this factor causes discontinuation of its effect on gene expression. Stable transcription factor binding may be a characteristic of nondividing cells.

scholarly journals Regulatory plasticity within a complex cytokine-sensing mammary enhancer during lactation

2020 ◽  
Author(s):  
Hye Kyung Lee ◽  
Chengyu Liu ◽  
Lothar Hennighausen
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Transcription Factor Binding ◽  
Mouse Genetics ◽  
Biological Information ◽  
Mammary Tissue ◽  
Factor Binding ◽  
Biological Importance

AbstractEnhancers are transcription factor platforms that synergize with promoters to activate gene expression up to several-thousand-fold. While genome-wide structural studies are used to predict enhancers, the in vivo significance is less clear. Specifically, the biological importance of individual transcription factors within enhancer complexes remains to be understood. Here we investigate the structural and biological importance of individual transcription factor binding sites and redundancy among transcription components within a complex enhancer in vivo. The Csn1s2b gene is expressed exclusively in mammary tissue and activated several thousand-fold during pregnancy and lactation. Using ChIP-seq we identified a complex lactation-specific candidate enhancer that binds multiple transcription factors and coincides with activating histone marks. Using experimental mouse genetics, we determined that deletion of canonical binding motifs for the transcription factors NFIB and STAT5, individually and combined, had a limited biological impact. Loss of these sites led to a shift of transcription factor binding to juxtaposed sites, suggesting exceptional plasticity that does not require direct protein-DNA interactions. Additional deletions revealed the critical importance of a non-canonical STAT5 binding site for enhancer activity. Our data also suggest that enhancer RNAs are not required for the activity of this specific enhancer. While ChIP-seq experiments predicted an additional candidate intronic enhancer, its deletion did not adversely affect gene expression, emphasizing the limited biological information provided by structural data. Our study provides comprehensive insight into the anatomy and biology of a composite mammary enhancer that activates its target gene several hundred-fold during lactation.

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 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 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 Integrating binding and expression data to predict transcription factors combined function

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Mahmoud Ahmed ◽  
Do Sik Min ◽  
Deok Ryong Kim
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Regulatory Region ◽  
Transcription Factor Binding ◽  
Expression Data ◽  
Dominant Type ◽  
Factor Binding ◽  
Two Factors

Abstract Background Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always lead to a functional interaction. Results We propose a method to predict the combined functions of two factors using comparable binding and expression data (target). We based this method on binding and expression target analysis (BETA), which we re-implemented in R and extended for this purpose. target ranks the factor’s targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets. Conclusion We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of two transcription factors.

scholarly journals Quantitative models for accelerated protein dissociation from nucleosomal DNA

2014 ◽  
Vol 42 (15) ◽  
pp. 9753-9760 ◽  
Author(s):  
Cai Chen ◽  
Ralf Bundschuh
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Specific Binding ◽  
Regulation Of Gene Expression ◽  
Transcription Factor Binding ◽  
General Mechanism ◽  
Binding Model ◽  
Factor Binding ◽  
Protein Dissociation

Abstract Binding of transcription factors to their binding sites in promoter regions is the fundamental event in transcriptional gene regulation. When a transcription factor binding site is located within a nucleosome, the DNA has to partially unwrap from the nucleosome to allow transcription factor binding. This reduces the rate of transcription factor binding and is a known mechanism for regulation of gene expression via chromatin structure. Recently a second mechanism has been reported where transcription factor off-rates are dramatically increased when binding to target sites within the nucleosome. There are two possible explanations for such an increase in off-rate short of an active role of the nucleosome in pushing the transcription factor off the DNA: (i) for dimeric transcription factors the nucleosome can change the equilibrium between monomeric and dimeric binding or (ii) the nucleosome can change the equilibrium between specific and non-specific binding to the DNA. We explicitly model both scenarios and find that dimeric binding can explain a large increase in off-rate while the non-specific binding model cannot be reconciled with the large, experimentally observed increase. Our results suggest a general mechanism how nucleosomes increase transcription factor dissociation to promote exchange of transcription factors and regulate gene expression.

scholarly journals Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Guohua Wang ◽  
Fang Wang ◽  
Qian Huang ◽  
Yu Li ◽  
Yunlong Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Interferon Treatment ◽  
Factor Binding ◽  
Hypersensitive Sites

Transcription factors are proteins that bind to DNA sequences to regulate gene transcription. The transcription factor binding sites are short DNA sequences (5–20 bp long) specifically bound by one or more transcription factors. The identification of transcription factor binding sites and prediction of their function continue to be challenging problems in computational biology. In this study, by integrating the DNase I hypersensitive sites with known position weight matrices in the TRANSFAC database, the transcription factor binding sites in gene regulatory region are identified. Based on the global gene expression patterns in cervical cancer HeLaS3 cell and HelaS3-ifnα4h cell (interferon treatment on HeLaS3 cell for 4 hours), we present a model-based computational approach to predict a set of transcription factors that potentially cause such differential gene expression. Significantly, 6 out 10 predicted functional factors, including IRF, IRF-2, IRF-9, IRF-1 and IRF-3, ICSBP, belong to interferon regulatory factor family and upregulate the gene expression levels responding to the interferon treatment. Another factor, ISGF-3, is also a transcriptional activator induced by interferon alpha. Using the different transcription factor binding sites selected criteria, the prediction result of our model is consistent. Our model demonstrated the potential to computationally identify the functional transcription factors in gene regulation.

scholarly journals Integrating transcription factor binding site information with gene expression datasets

2006 ◽  
Vol 23 (3) ◽  
pp. 298-305 ◽  
Author(s):  
I. B. Jeffery ◽  
S. F. Madden ◽  
P. A. McGettigan ◽  
G. Perriere ◽  
A. C. Culhane ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Transcription Factor Binding ◽  
Factor Binding Site ◽  
Factor Binding

scholarly journals Regulation of spatiotemporal limits of developmental gene expression via enhancer grammar

2020 ◽  
Vol 117 (26) ◽  
pp. 15096-15103 ◽  
Author(s):  
Samuel H. Keller ◽  
Siddhartha G. Jena ◽  
Yuji Yamazaki ◽  
Bomyi Lim
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcriptional Regulation ◽  
Binding Site ◽  
Binding Sites ◽  
Transcriptional Activity ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Fine Tuning ◽  
Factor Binding

The regulatory specificity of a gene is determined by the structure of its enhancers, which contain multiple transcription factor binding sites. A unique combination of transcription factor binding sites in an enhancer determines the boundary of target gene expression, and their disruption often leads to developmental defects. Despite extensive characterization of binding motifs in an enhancer, it is still unclear how each binding site contributes to overall transcriptional activity. Using live imaging, quantitative analysis, and mathematical modeling, we measured the contribution of individual binding sites in transcriptional regulation. We show that binding site arrangement within the Rho-GTPase componentt48enhancer mediates the expression boundary by mainly regulating the timing of transcriptional activation along the dorsoventral axis ofDrosophilaembryos. By tuning the binding affinity of the Dorsal (Dl) and Zelda (Zld) sites, we show that single site modulations are sufficient to induce significant changes in transcription. Yet, no one site seems to have a dominant role; rather, multiple sites synergistically drive increases in transcriptional activity. Interestingly, Dl and Zld demonstrate distinct roles in transcriptional regulation. Dl site modulations change spatial boundaries oft48, mostly by affecting the timing of activation and bursting frequency rather than transcriptional amplitude or bursting duration. However, modulating the binding site for the pioneer factor Zld affects both the timing of activation and amplitude, suggesting that Zld may potentiate higher Dl recruitment to target DNAs. We propose that such fine-tuning of dynamic gene control via enhancer structure may play an important role in ensuring normal development.

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