scholarly journals EFFECT OF GLUCOCORTICOIDS ON PIT 1 INHIBITION OF SV40 ENHANCER ACTIVITY

2010 ◽  
Vol 5 (1) ◽  
pp. 113-118
Author(s):  
S. El-Jaafary ◽  
A. B. EL Fighi ◽  
S. A .B. Hasan
1988 ◽  
Vol 154 (1) ◽  
pp. 483-488
Author(s):  
Kimitoshi Kohno ◽  
Yukihide Iwamoto ◽  
George R. Martin ◽  
Yoshihiko Yamada

1988 ◽  
Vol 16 (21) ◽  
pp. 10171-10181
Author(s):  
Kaoru Kikuchi ◽  
Mayumi Sekikawa ◽  
Masanori Imoto ◽  
Tomofusa Tsuchiya ◽  
Masaaki Tsuda

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1706-P ◽  
Author(s):  
ARUSHI VARSHNEY ◽  
STEPHEN PARKER ◽  

2021 ◽  
Vol 49 (7) ◽  
pp. 3856-3875
Author(s):  
Marina Kulik ◽  
Melissa Bothe ◽  
Gözde Kibar ◽  
Alisa Fuchs ◽  
Stefanie Schöne ◽  
...  

Abstract The glucocorticoid (GR) and androgen (AR) receptors execute unique functions in vivo, yet have nearly identical DNA binding specificities. To identify mechanisms that facilitate functional diversification among these transcription factor paralogs, we studied them in an equivalent cellular context. Analysis of chromatin and sequence suggest that divergent binding, and corresponding gene regulation, are driven by different abilities of AR and GR to interact with relatively inaccessible chromatin. Divergent genomic binding patterns can also be the result of subtle differences in DNA binding preference between AR and GR. Furthermore, the sequence composition of large regions (>10 kb) surrounding selectively occupied binding sites differs significantly, indicating a role for the sequence environment in guiding AR and GR to distinct binding sites. The comparison of binding sites that are shared shows that the specificity paradox can also be resolved by differences in the events that occur downstream of receptor binding. Specifically, shared binding sites display receptor-specific enhancer activity, cofactor recruitment and changes in histone modifications. Genomic deletion of shared binding sites demonstrates their contribution to directing receptor-specific gene regulation. Together, these data suggest that differences in genomic occupancy as well as divergence in the events that occur downstream of receptor binding direct functional diversification among transcription factor paralogs.


2021 ◽  
Author(s):  
Mourad Wagdy Ali ◽  
C. Pawan K. Patro ◽  
Matthew Devall ◽  
Christopher H. Dampier ◽  
Sarah J. Plummer ◽  
...  

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Henriette Miko ◽  
Yunjiang Qiu ◽  
Bjoern Gaertner ◽  
Maike Sander ◽  
Uwe Ohler

Abstract Background Co-localized combinations of histone modifications (“chromatin states”) have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points (“chromatin state trajectories”) have previously been analyzed at promoter and enhancers separately. With the advent of time series Hi-C data it is now possible to connect promoters and enhancers and to analyze chromatin state trajectories at promoter-enhancer pairs. Results We present TimelessFlex, a framework for investigating chromatin state trajectories at promoters and enhancers and at promoter-enhancer pairs based on Hi-C information. TimelessFlex extends our previous approach Timeless, a Bayesian network for clustering multiple histone modification data sets at promoter and enhancer feature regions. We utilize time series ATAC-seq data measuring open chromatin to define promoters and enhancer candidates. We developed an expectation-maximization algorithm to assign promoters and enhancers to each other based on Hi-C interactions and jointly cluster their feature regions into paired chromatin state trajectories. We find jointly clustered promoter-enhancer pairs showing the same activation patterns on both sides but with a stronger trend at the enhancer side. While the promoter side remains accessible across the time series, the enhancer side becomes dynamically more open towards the gene activation time point. Promoter cluster patterns show strong correlations with gene expression signals, whereas Hi-C signals get only slightly stronger towards activation. The code of the framework is available at https://github.com/henriettemiko/TimelessFlex. Conclusions TimelessFlex clusters time series histone modifications at promoter-enhancer pairs based on Hi-C and it can identify distinct chromatin states at promoter and enhancer feature regions and their changes over time.


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